{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>BLKGRPCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
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       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>031</td>\n",
       "      <td>700903</td>\n",
       "      <td>1</td>\n",
       "      <td>240317009031</td>\n",
       "      <td>Block Group 1</td>\n",
       "      <td>G5030</td>\n",
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       "      <td>3837935</td>\n",
       "      <td>18627</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>16</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>POLYGON ((-77.14346 39.09325, -77.14278 39.093...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>031</td>\n",
       "      <td>701101</td>\n",
       "      <td>4</td>\n",
       "      <td>240317011014</td>\n",
       "      <td>Block Group 4</td>\n",
       "      <td>G5030</td>\n",
       "      <td>S</td>\n",
       "      <td>431071</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>POLYGON ((-77.12805 39.07815, -77.12802 39.078...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>033</td>\n",
       "      <td>803508</td>\n",
       "      <td>2</td>\n",
       "      <td>240338035082</td>\n",
       "      <td>Block Group 2</td>\n",
       "      <td>G5030</td>\n",
       "      <td>S</td>\n",
       "      <td>930573</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-76.87428 38.92641, -76.87381 38.927...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>033</td>\n",
       "      <td>803508</td>\n",
       "      <td>3</td>\n",
       "      <td>240338035083</td>\n",
       "      <td>Block Group 3</td>\n",
       "      <td>G5030</td>\n",
       "      <td>S</td>\n",
       "      <td>273692</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-76.86473 38.92082, -76.86309 38.922...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>24</td>\n",
       "      <td>033</td>\n",
       "      <td>803509</td>\n",
       "      <td>1</td>\n",
       "      <td>240338035091</td>\n",
       "      <td>Block Group 1</td>\n",
       "      <td>G5030</td>\n",
       "      <td>S</td>\n",
       "      <td>1091937</td>\n",
       "      <td>3091</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-76.88412 38.93630, -76.88373 38.936...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 346 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20 BLKGRPCE20       GEOID20     NAMELSAD20  \\\n",
       "0        24        031    700903          1  240317009031  Block Group 1   \n",
       "1        24        031    701101          4  240317011014  Block Group 4   \n",
       "2        24        033    803508          2  240338035082  Block Group 2   \n",
       "3        24        033    803508          3  240338035083  Block Group 3   \n",
       "4        24        033    803509          1  240338035091  Block Group 1   \n",
       "\n",
       "  MTFCC20 FUNCSTAT20  ALAND20  AWATER20  ... P0050002 P0050003 P0050004  \\\n",
       "0   G5030          S  3837935     18627  ...       16        0       16   \n",
       "1   G5030          S   431071         0  ...        0        0        0   \n",
       "2   G5030          S   930573         0  ...       11        0        0   \n",
       "3   G5030          S   273692         0  ...        0        0        0   \n",
       "4   G5030          S  1091937      3091  ...        0        0        0   \n",
       "\n",
       "  P0050005 P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        0       97        0        0       97   \n",
       "1        0        0       11        0        0       11   \n",
       "2        0       11        0        0        0        0   \n",
       "3        0        0        0        0        0        0   \n",
       "4        0        0        0        0        0        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-77.14346 39.09325, -77.14278 39.093...  \n",
       "1  POLYGON ((-77.12805 39.07815, -77.12802 39.078...  \n",
       "2  POLYGON ((-76.87428 38.92641, -76.87381 38.927...  \n",
       "3  POLYGON ((-76.86473 38.92082, -76.86309 38.922...  \n",
       "4  POLYGON ((-76.88412 38.93630, -76.88373 38.936...  \n",
       "\n",
       "[5 rows x 346 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/md_pl2020_bg.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 4079 popn tracts for MD\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"MD\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population MD voter-age population\n",
      "state pop= 6177224 VAP pct Hispanic is  0.102230809839338\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP MD\n",
      "total state pop= 6177224 , VAP pct Black is  0.29158797491777083\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for MD\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for MD\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "2371 1504 0.0002609262114999811 nonPolygon tract no, pop, area\n",
      "6.051354999951282e-07\n",
      "0.00026032107599998597\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "69edb6b2-7b88-43ca-8017-ff4d271ce3e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#special for MD tract 2371 - re-assign the tract to its latter polygon\n",
    "problemTract = [2371]\n",
    "for pT in range(len(problemTract)):\n",
    "    t = problemTract[pT]\n",
    "    counter = 0\n",
    "    for geom in tractGeom[t].geoms:\n",
    "        print(counter)\n",
    "        if counter == 1:  #here, the latter geom is the good one\n",
    "            fixedTract = geom\n",
    "        counter +=1\n",
    "    tractGeom[t] = fixedTract\n",
    "    notPoly[t] = 0\n",
    "    x,y = tractGeom[t].exterior.xy\n",
    "    plt.plot(x,y)\n",
    "    plt.text(tractGeom[t].centroid.x,tractGeom[t].centroid.y,t)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state.  not needed for MD\n",
    "for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>NUMBER</th>\n",
       "      <th>JURSCODE</th>\n",
       "      <th>VOTESPRE</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PREBSEG</th>\n",
       "      <th>G20PREOWRI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HOWARD PRECINCT 06-001</td>\n",
       "      <td>06-001</td>\n",
       "      <td>HOWA</td>\n",
       "      <td>006-001</td>\n",
       "      <td>1005</td>\n",
       "      <td>402</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>POLYGON Z ((1360475.813 539373.556 0.000, 1360...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HOWARD PRECINCT 05-023</td>\n",
       "      <td>05-023</td>\n",
       "      <td>HOWA</td>\n",
       "      <td>005-023</td>\n",
       "      <td>1116</td>\n",
       "      <td>190</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>POLYGON Z ((1343191.760 567973.996 0.000, 1343...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HOWARD PRECINCT 05-018</td>\n",
       "      <td>05-018</td>\n",
       "      <td>HOWA</td>\n",
       "      <td>005-018</td>\n",
       "      <td>896</td>\n",
       "      <td>159</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>POLYGON Z ((1344525.177 561849.349 0.000, 1344...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HOWARD PRECINCT 05-017</td>\n",
       "      <td>05-017</td>\n",
       "      <td>HOWA</td>\n",
       "      <td>005-017</td>\n",
       "      <td>1085</td>\n",
       "      <td>290</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>POLYGON Z ((1344438.747 561396.875 0.000, 1344...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HOWARD PRECINCT 05-020</td>\n",
       "      <td>05-020</td>\n",
       "      <td>HOWA</td>\n",
       "      <td>005-020</td>\n",
       "      <td>1188</td>\n",
       "      <td>663</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>POLYGON Z ((1336832.452 540093.639 0.000, 1336...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     NAME  NUMBER JURSCODE VOTESPRE  G20PREDBID  G20PRERTRU  \\\n",
       "0  HOWARD PRECINCT 06-001  06-001     HOWA  006-001        1005         402   \n",
       "1  HOWARD PRECINCT 05-023  05-023     HOWA  005-023        1116         190   \n",
       "2  HOWARD PRECINCT 05-018  05-018     HOWA  005-018         896         159   \n",
       "3  HOWARD PRECINCT 05-017  05-017     HOWA  005-017        1085         290   \n",
       "4  HOWARD PRECINCT 05-020  05-020     HOWA  005-020        1188         663   \n",
       "\n",
       "   G20PRELJOR  G20PREGHAW  G20PREBSEG  G20PREOWRI  \\\n",
       "0          30           6           5          11   \n",
       "1          14           4           2           9   \n",
       "2           8           2           1           9   \n",
       "3          14           6           3          13   \n",
       "4          22           6           1          21   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON Z ((1360475.813 539373.556 0.000, 1360...  \n",
       "1  POLYGON Z ((1343191.760 567973.996 0.000, 1343...  \n",
       "2  POLYGON Z ((1344525.177 561849.349 0.000, 1344...  \n",
       "3  POLYGON Z ((1344438.747 561396.875 0.000, 1344...  \n",
       "4  POLYGON Z ((1336832.452 540093.639 0.000, 1336...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/md_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>NUMBER</th>\n",
       "      <th>JURSCODE</th>\n",
       "      <th>VOTESPRE</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PREBSEG</th>\n",
       "      <th>G20PREOWRI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HOWARD PRECINCT 06-001</td>\n",
       "      <td>06-001</td>\n",
       "      <td>HOWA</td>\n",
       "      <td>006-001</td>\n",
       "      <td>1005</td>\n",
       "      <td>402</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>POLYGON Z ((-76.83025 39.14757 0.00000, -76.83...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HOWARD PRECINCT 05-023</td>\n",
       "      <td>05-023</td>\n",
       "      <td>HOWA</td>\n",
       "      <td>005-023</td>\n",
       "      <td>1116</td>\n",
       "      <td>190</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>POLYGON Z ((-76.89107 39.22616 0.00000, -76.89...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HOWARD PRECINCT 05-018</td>\n",
       "      <td>05-018</td>\n",
       "      <td>HOWA</td>\n",
       "      <td>005-018</td>\n",
       "      <td>896</td>\n",
       "      <td>159</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>POLYGON Z ((-76.88639 39.20934 0.00000, -76.88...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HOWARD PRECINCT 05-017</td>\n",
       "      <td>05-017</td>\n",
       "      <td>HOWA</td>\n",
       "      <td>005-017</td>\n",
       "      <td>1085</td>\n",
       "      <td>290</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>POLYGON Z ((-76.88670 39.20810 0.00000, -76.88...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HOWARD PRECINCT 05-020</td>\n",
       "      <td>05-020</td>\n",
       "      <td>HOWA</td>\n",
       "      <td>005-020</td>\n",
       "      <td>1188</td>\n",
       "      <td>663</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>POLYGON Z ((-76.91361 39.14963 0.00000, -76.91...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     NAME  NUMBER JURSCODE VOTESPRE  G20PREDBID  G20PRERTRU  \\\n",
       "0  HOWARD PRECINCT 06-001  06-001     HOWA  006-001        1005         402   \n",
       "1  HOWARD PRECINCT 05-023  05-023     HOWA  005-023        1116         190   \n",
       "2  HOWARD PRECINCT 05-018  05-018     HOWA  005-018         896         159   \n",
       "3  HOWARD PRECINCT 05-017  05-017     HOWA  005-017        1085         290   \n",
       "4  HOWARD PRECINCT 05-020  05-020     HOWA  005-020        1188         663   \n",
       "\n",
       "   G20PRELJOR  G20PREGHAW  G20PREBSEG  G20PREOWRI  \\\n",
       "0          30           6           5          11   \n",
       "1          14           4           2           9   \n",
       "2           8           2           1           9   \n",
       "3          14           6           3          13   \n",
       "4          22           6           1          21   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON Z ((-76.83025 39.14757 0.00000, -76.83...  \n",
       "1  POLYGON Z ((-76.89107 39.22616 0.00000, -76.89...  \n",
       "2  POLYGON Z ((-76.88639 39.20934 0.00000, -76.88...  \n",
       "3  POLYGON Z ((-76.88670 39.20810 0.00000, -76.88...  \n",
       "4  POLYGON Z ((-76.91361 39.14963 0.00000, -76.91...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2043 0.3297095295290766 2961437 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#see NJ code for convex-hulling tracts\n",
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        for geom in vtdGeom[p].geoms:    \n",
    "            xs, ys = geom.exterior.xy\n",
    "            plt.plot(xs,ys)\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "isSkippedTract = [0]*nTracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5520a7c2-0560-421c-b5ef-bfd8aa9752b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ua0llamFKalwqMzrOYNHQRSSevjvPW4XiUeSxFvSbP9pM9LpoQB/FMPSL0PIdkMJApXqVGLxgMBb2Fhybeey2dVPjUjG1NL3tj1Pz15vjHexNfk4+0WujmRYyje1fbSc/R/24Kyo+j3VQs4thFwt9Pj77ODu/2YlfZz9yUnPo9Xcv7D3tDYmjFQ8WM2szvIO9Ob/t/G3rZSXc2WvXO8gb7yBvQL8xu+GdDWz9ZCtm1mYqbaCiwvNYr+if3fYsvh31Aa06fduJ1It6X66YjTHE7Y1j/dj1fOf6HV+IL9TKr5zwDvEm8XQiaZfS7lufVZtWpcNXHQCKbPYqFBWRx1rQAwR9GATow+SOOjQKnxAfAAbOG4h3iLeh3teWX5OTllMeQ3ysufH/OLftXLF1zGzMsHIpncC2dtM/pcWfjL/boSkUjwyPteoGYEbHGYb3O7/ZyfANw0mITDCkstNmawkbH4aFvQXaXG15DfOxpXLDyljYW3Au9Bz1nqxntI7GTIM2r3T/G9earjR8tiF7ft5Dk5ea4Fzd+Y5trl+4zp6f91CtTbV7jpmvUDxIHvsV/bPbnsXe057mrzen4/iOaMw0hfKVdvxfRz6Tn/H+9feVrr4cMNGY6PX0ocXr6TXmGnR5umLLi6PD1x1AwoG/7mzKe2LhCX6v+Tt7ftpjSHmoUDwqPPaC3jvYm7EXx9L9l+73nOxaUTYY9PSXjevpNeYadPmlF/R2Ve2o0bMG4XPDKc6fRErJjv/tYNGQRQbz2+pdq5f6XApFefLYC3rFw88NPX1x1jemlqZI3e0d/zZ/tJnv3L/jp2o/ce34f+Gqfdr7kBaXRsbVjCJtpJSsHbOWLR9tIaB3ADbuNrjWcqXhMw3vei4KRXnw2OvoFQ8/N/TnNxyjbsWYoM9JyyFicQTR66K5sOMCaZfSEBqB1Erm9Z/H69GvF6qvMdcU6Xfrp1vZP3E/Nu42pF5MJTMhk6HLhxqtq1A8zChBr3jouXrsKoAhkfitmFqZgoR1Y9dxftt5kqKSyE2/KT2CAM/Wnjy98Wmmtp3KlcNXiNsfh0czD3LT9PXMbMwK9Xl8znF2fLUD0MfSN7c1p/vv3fFs6VkGM1QoyhYl6BUPPZcOXgL0YYqNkZeRB8Den/XJy8ztzKnStArewd4EDgjEs5UnJiZ6LaWlkyUA2hy9lc6NH4T0y+k4+jgCkHYpjRUvrDD03+6zdrT7rJ0Kbax4ZFGCXvHQE38iHisXK2wrFZ94BKDvtL7UHlQbcxtzQB+cLXZ3LBlXMwwRKy9sv4C5nTmerT058NcBdn67kxo9axTKabv3t70GU1rnGs60+1QJecWjjRL05cRvNX4jKToJazdrMuMzMbU05aOsj8p7WA8ltpVtyUrMYs8ve2j5RtE8r8lnk7F0tCy0SXpk+hFWvrgSXZ4Ou6p2vH7mdb0uX0rMbc3Z9d0uNr+/mYBeAQxeOLhQ5NK4PXG41nLF1MKUy4cvk3AqQSURVzzSKKubcuLGijEzPhOA/Ox8IpZElOeQHlqCPgqiVv9arH9zPRve3VBo4/XSwUvkZ+VTtWlVw7GoNVGseG4F3sHeBH8STNqlNC7s1EfArFS/EumX09n8/mbqDq3LkCVDigRD8w7xJv5EvD48hoSEyIQHM1GFooxQgr6ceOPcG3T4XwfqDKljOGZuZ16OI3p4MbMyY/DCwTR9tSm7v9/N8pHLDXbv+37fB0DTl/VhuKWUbP5gMy41XRi6bCjewfowFtnJ+gQlnSd0BvT6/v6z+qMxK2pBE/RhEFWbVWXX97sAve4/LyuvbCepUJQhdxT0QghLIcQ+IcRRIcQJIcQXBccbCCF2CyGOCyFWCiHsi2nfTQhxSggRLYR4/35P4GElel00cfviii0XQhD0QRCD5g/i9TOv89zO56jeWTniFIeJxoQev/egxZstODrjKNfPXwf0AeiEiaBmP32u2KToJK4eu0qz0c0wtzWnWptq2FezZ//E/QD4dfLDrbYblw9eLtb2XmOmoc+UPggTgbWbNed3nGfrp1sfzEQVijKgJCv6HKCDlLIB0BDoJoRoCUwG3pdS1gOWAu/e2lAIoQEmAt2B2sCTQogKHyTkjzp/MLv7bCa3mFysx+XNOPk5Ua11tQcwskebhMgEzm09hzARmJiZkH4tnbS4NFwDXTHR6G/l6xf0PwDuddwB/dNAQO8ALh+6zLXwa/zb7l9DILNp7abpc9AaoVK9StQfVp+8jDyqd6nO4SmHSx1PR6F4WLijoJd60gs+mhW8JFAT2F5wfCMw0Ejz5kC0lDJGSpkLzAP63vOoH2KklIUiIuZnqfDGd0t+dj6JUfpMUEdnHGVS40mkxaXx5MonsfewZ8PbG4D/1DaAwTrmZlVL7UG1yUnN4c96f3Jh+wVsq9hSa0Atks8kM7X1VOb2nkvM5phCgjwhMoH4k/HkZeYRfzKe3LTcO3rfKhQPKyWyuilYmR8E/IGJUsq9QohwoA+wHBgMGFuSegA3L5ligRbFnGMUMArAy8urpON/6BBC8H7q++z9dS81etTAzNrszo0UhTi18hT7fttHzMYYABq/2JhDkw9RrVU1hiwegm1lW04uPsnxWccxtzWn6at6QZ8am8q6N9ZhamlayLnKt72v4b17XXeGrx+OXVU7ctNz2fvrXnZ9v4vTq05jbmuOay1X8rPzuRb+X5iE9MvpdP+9O6YWykhN8WhSquTgQghH9GqaMUA+8CvgAqwAXpdSutxSfzDQVUr5QsHnEUBzKeWY253nQSYHv8GN66DspcuPG/+Dub3nErU6qkj5G+fewNHbkZz0HL53+x5trpZRB0dRuWFlAH6v+TuJpxMZvmF4kf2OvKw8No7byP7f9bp6azdrzKzM0FhosKtqhzZHi52HHblpuejydZzdchaAqs2q0mdyn2K9chWKh4XbJQcv1RJFSpkihAgFukkpvwe6FJwgAOhppEkshVf6nsCl0pzzQbFzwk42v78ZAP9u/vh29KX1O63LeVSPDxvHbeTwlMOYWpqSlZSFWx03Wrzegp3f7gRg2LphOHo7AjCn5xzys/MJ+jjIIORPLDxB4ulE6o+ob3RT28zKjB6/9aDpS02JXh9N4ulEtDlaw+o9/kQ8GgsNAb0COLtZL+Rbjm1Jp287GbXMUSgeJe4o6IUQbkBegZC3AjoB3woh3KWU14QQJsDHwF9Gmu8HagghfIE4YCjw1P0b/v3jZoeZ6HXRXAu/pgT9A+TQ5ENoc7VkJenzvzZ4ugFNRjWhyagmhepFLIngwvYLOPo60uFLfTrAmM0xLBm2BK+2XvT6q9dtz+Ne1x33uu5Fjl/cdZFZXWdxIewCNXrWoMmoJgbTTIXiUackK/oqwPQCPb0JsEBKuUoI8YYQYnRBnSXAvwBCiKrAZCllDyllvhDiNWA9oAGmSilP3P9p3Dtt3m1Dk1FNOLnwJPk5+fh38y/vIT1WmFqYUqtfLUI+DyEzIdOoqkSn07Hs2WUgYNjaYfpjWh1rX1uLk68TT6568q73RKq1rsa7Ce+iMdco9Z2iwnFHQS+lPAY0MnL8F+AXI8cvAT1u+rwGWHNvw3wwWDpY0viFxuU9jMeSyo0qc2r5KbzaelH3ybqYmBY1CFszeg25abk0fLYhrjVdAX2UyYTIBAYvGoylg+U9jUFttioqKsozVvFQ0O3nblg6WrLi+RVMD5lOZmJmofL0a+kcmnQIMxszev/T23B832/7cK/rTuCAwAc9ZIXikUEtYRQPBS4BLrwS/gr7ft/HpnGb2PfbPkI+DzGUbxq3CamTdBzf0bDaTziVwKX9l+jyQ5e7UrdcCLvA6dWnSTmbgtRJ3Ou60/Tlpti429yvaSkUDwVK0CseGvIy8tj3qz52jWst10JlEUsi0JhraDa6meFY5LJIAOo8UYc7IXWSw1MPE7kskh6/98DRx5GVo1aSEJGAs78+g9XJhSe5uPMi/Wf2V8JeUaFQqhtFuXFq5SkilkQY7OcjlkaQGptK/1n9qTu0rqFezOYYctNy8engY0ggAvocsu513bH3MBpmyUDK+RSmd5jOyhdXErU6ivB54QB4tvBEY6Hh+d3PMyZqDO2/bM+ZDWdY/9b6MpitQlF+KEGvKBei10Uzr888FgxcwLTgaVwLv4ZXW71H9K2Jurd/qY+00f7/2hc6nhSdhFvtO8eJX/3yas5vO0/vyb3xCvJi6ydbObHwBK3fbY02R8u+ifqniKCPgtBYaLB0vLdNXYXiYUMJekW5ELs31vD+6vGr/Bv8Lxd26GPGZ8T/J+illFzcdRFLR0s8mnkU6iM7ORsrF6tCx3Izctkxfgc7J+xEp9UB4BzgjDAR+LTz4alVT+HRwoPFQxeTmZCJbwdfQ2RLqZVoc7TYVFJqG0XFQunoFeVCyzdaknYpjUOTDtFnSh+2fLSFVS+tAsAnxMdQ7+DfB9Hl6chOyWZW91kg9cJf6iSZCZnk5xQOGrf9y+0Gb1qdVkfrt1tj72mP1El+q/EbgxYMYtD8Qfzk+RPzB8wnKzHLkAdAmAg0FhqD05ZCUVFQgl5RLlg6WtL0paYcmnQIYSJ4+ejLxO6JxdzGvFAS8ONzjxven1l3pkg/Nwcfu/lz1aZV2frJVnZ+s5Oc1BxD+doxa3n78tvUG1aPs1vO0vDZhrR6pxWgF/QezT2I3RWLQlGRUIJeUW7cCEF8cuFJAvsH4tPOp0idzGt6e/q34t7Se61qBEIjMDExYUqbKdi6F04YrsvTUalBJYavH87OCTvJSs6iVt9a2FW1I3JZJP49/Dmz/gxeQV60/aCtIW79DbzaerHru13kZuQakowrFI86StArHjhSSrZ/tZ3Qz0IBCP4kuNi6yWeTsXa1xq6qXZEyKycrspILq1lca7tyfsd5LB0t6fRNJ8Px/Jx8kn/Ux5+X2v8itgYODGTIoiGGzw5eDujydXqVjhL0igqC2oxVPFCklKx8cSWhn4ZSf1h9Pkj/ALdA45YzyeeS0eZojce9ydcRfyIeJz+nQsfdAt3Iz8onNTbVcCxsQhjfOHzDsZnHMLMyo8EzDQjoE4DQCCIWR7D06aWGugmRCZhZm2HveXuTTYXiUUKt6BUPlPysfI7POY6DlwN9pvRBY158COBjM48B4N+jaIC5Y7OPkZmQSeDAwqEPTC31t3ROWg5x++KY338+aZfSMDE1odU7rWg5tiXLn1lOzKaYQucxMTWh79S+XD16FbfaboWimSoUjzpqRa94oJhZ62PVXL9wnf/Z/o+Dkw4WW/fMBv3ma71h9Qodl1Ky7fNtVGlShYCeAcTujWXtG2tZ9coq1r2xDnNbc/b8vIfJLSaTdikNv85+vBv/LvWH12dKiylc3HUR3476rFP1h9cH4Mi/R0g8ncjFXReNhjFWKB5llKBXPHDqD6tPr797YW5rzvqx60k5l2K03vXz1zExM8GucmH9fPrldFLOpdDgmQZc3HWRqW2mcuifQ4TPDcejuQe+HX05PPkw5nbmPL31aUZsGMGVI1f4t+2/SClp/2V7Q3KR+k/XN/RrYmaCbWVbjkw7wqmVp8ps/grFg0YJekW50GRUE14+8jL5Ofkc+Mt42sjslGyjG6I3En8nRSWx7f+2YeVkxduX3ub9lPdp9U4rTi0/hYW9BW/FvYVviC+JUYnMHzAfOw87Xtz3Ig2eaYCjryMACwctBKB6t+o4+ToxfP1wAOJPxBc5r0LxqKJ09Ipyw9LJEqmVmNsat27Jy8zDwcuhyHGHag44+jqy7zd96II277fB0tGSnLQc5vefD8DwDcPJTctlXt95nNt6DgSM3DHSYL3Te1JvFg5ZCAUGOG613dj9024y4/XmnDfb8isUjzpK0CvKjYglEQB4tvQsUqbN0yK10mg4Ao25hpePvMyFsAtcv3idhs80JCM+g0mNJ5GXkUeLsS3wbOHJwsELid0TS6t3WlG9S/VCNvN+nfx4L+k9Tiw4wZrX1nDwr4PkZeqfFPy7+ePXya+MZq1QPHhKkjPWEtgOWBTUXySl/EwI0RB9nlhLIB94VUq5z0j7scAL6NdOx4GRUsrs+zYDxSPL2U1nsXazNmyM3kxCZAIADp5FV/QAFvYW1OhRg9zMXNaPXc/Bfw4itZKA3gF0+7Eb1y9eJ2JpBK3ebkXnbzsXO4Y6Q+pQZ4g+zHHSmSSuHrtKje41VDpBRYWiJCv6HKCDlDJdCGEGhAkh1gL/B3whpVwrhOgBTABCbm4ohPAAXgdqSymzhBAL0CcIn3Yf56B4BMm4lsGplaeo0cO4UF32zDIATi46ybcu36LN0aLN1a/yEfpVvcZcow9vIMHUypTO33Wm+ejmAByffRyplTR7pVmRvovDubozztWd78v8FIqHiZLkjJVAesFHs4KXLHjd8CpxAC7d5hxWQog8wPo29RSPATmpORyfe5wNb21Am6el3aftjNa7dlwfs8bU0hRdrg5TS1OsnK0wszFDl6cjJy2H/Ox8nHydqDesHsGfBKMx+88mP3ZPLE7VnXD0cXwQ01IoHmpKpKMXQmiAg4A/MFFKuVcI8SawXgjxPXrrnda3tpNSxhWUXwCygA1Syg3FnGMUMArAy8vrLqaieNg5ufgky59dTm56LgDBnwYXySQFkJWchS5fh2ugK6NPji62v/ycfKMJvSOXRXJ65elC2agUiseZEgl6KaUWaCiEcASWCiHqohfKY6WUi4UQQ4ApQKeb2wkhnIC+gC+QAiwUQgyXUs4yco5JwCSApk2bylvLFY82unwd2/9vOwh4Ye8LaMw1uNcz7pi06/tdANQfUb9ImdRJdv+4m7BvwshKzMLSyRKXGi4413DGuYYzdlXs2PzBZio1qETH/3Us0zkpFI8KpbK6kVKmCCFCgW7AM8AbBUULgclGmnQCzkop4wGEEEvQr/yLCHpFxUWbq2X92+u5euwq7b9sj0dzj9vWPzH/BAho8XqLImUrXlzBkalH8O/uj2crT9IupZEUlcSFHRc4Puc4SNBYaOjxe49izTYViseNkljduAF5BULeCr3w/ha9rr0dEAp0AKKMNL8AtBRCWKNX3XQEjHvHKCocV45eYVq7aZhampJxNYOmrzQl+OPiI1WC3nY+OSYZJ1+nIs5SCZEJHJl6hFbvtKLzhM5FNnHzs/NJjknG3M4ch2rGrXUUiseRkqzoqwDTC/T0JsACKeUqIUQK8IsQwhTIpkC/LoSoCkyWUvYo0OUvAg6hN8E8TIF6RlHx+bvh3wCYWZnx1Jqn8O9aNDjZrez9bS9IDCaPN5N+VW8TUL1LdaOWOqaWpiXKIatQPG6UxOrmGNDIyPEwoImR45eAHjd9/gz47N6GqXgUafthW8L+F8Yzoc/gWrPopqsxjs3QR6y8kfXpZpz9nREawZn1Z6jeufp9HatCUZFRsW4UZcYNXfyRaUdKVF9KScKpBGwr22LtYl2k3N7DnjqD67Dn5z0kn02+n0NVKCo0StAryowbCUVy03JLVP/S/ktIrcSzVdGQCDfo/J3ey3XjOxs5v/28IWwBQNeuXXFzc+Orr74q1Gbq1KmYmZkZPi9dupTAwEAszC2IXBaJLl8HwIoVK2jRogVBQUHMnj27ZJNUKB4BVKwbRZnhEuBCnSF1ODz1MBd2XMDOw44OX3egSiPjAcOOzdGrbWr2rVlsn/ae9jQf05y9P+8lYkkEQiOo3LAyDZ5uwF+//8W2nduIjY1Fl6/jyLQj5OTlsGTVEqpVq2boo2mdprzv+z5jI8cyv/98gj4OIuSLEN59910OHDiApaUlwcHB9OzZE0dHx/t5SRSKckEJekWZEvJ/IWQmZGJua07U2ijyMvN4NvRZo3XPh54HoPag2rfts9tP3Qj6MIi4vXFc3H2RmI0xrHtjHe6T3bF6ywopJYuGLiJicQRhhPHkN08SeSoS0Nvzr+i3guQzyVjaW0IqxO2NIyEhATc3N+zs9NEtAwIC2LdvH126dLl/F0OhKCeUoFeUKa41XXl689MA/NvuXxJPJbLlky2YWZthZmWm/2tjhrmNOQmRCZjbmZcoKbeNmw0BvQII6BVAx6878qXZl1jYWwBwIewCug06ar9Um9l/z8ZimwW5abkkn00mOyWbhIgEev7Vk78/0VsFBX0YhJubGwkJCcTFxWFnZ0dYWBjdu3cvuwujUDxAlKC/D2QmZhKxJILag2pj5WRV3sN5aEmKTiL9Sjo7vtpRbB2pK71TdNqlNHT5OgIHBnIs/Rhnt5ylU79O7LPbx/P9nid6eTTpMp1fq/9qsP7xaO6B0OhNNLOSsxBCMGnSJEaMGIGNjQ316tWjatWqdzdRheIhQwn6eyAvK48Dfx5g+5fbyU7JJmZjDIMXDC7vYT202HvYY+NuQ6uxrcjLyiMvI4+8zDzys/LJTMrk4F8HDavy0pBwSh/SuFL9SuQdzEOXr6Nyo8pEHYriePZxZCdJTlgOa+zW0CNSb/mbejEVc2tzKtWvxPKRy3Gr7UZwcDBbtmwhLS2N/v3706JFUc9cheJRRAn6uyRqbRRrX1tLcsx/Zn4nF54kdk+s0UQaCr1VDUCDpxsYLQ+fE442V1vqfnOu5wDwwfcfEH4hnATrBF7+/GXWbl2LTzsfAPz9/Vm5bCUzOs7gPOd5acJLXLp8iXmN5+Elvfin6T/s89pHilsKpmamjB8/HguL0v/oKBQPI0rQlxIpJateWsWhfw4ZjgkTgXtddyrVr4SDt3K9v1scfBy4duwaunwdJqYlt/y9fuE6AJP+noSDlwPZKdn8FvAb+3/fbxD00dHRALx95W2Q+v/ZDa4cvcLc3nNpeLIhYy+Oxd7Tvsg5FIpHGWVHX0rCxocVEvJt3m/DuMRxvHz0ZfrP7I9dFbtyHN3DjXewN97B3sWWV2msN7s8u/Vsqfq90e7SQf0TQ2aCPu/rDR38zQghCgl5gMoNKjNo/iAANr67kfycfACuX7xuyHSlUDzKqBV9KdDl69jxv/82EusOrUvH/3VUaedKiI27DdfCrxVb7tfJj6PTjhK9NrpUIQ6qNquKpZMle3/eS/zJeHZ8tQONuYbGLzYucR/VWlWj4/iObP5gM/ER8fi092HvL/q4O63eLgiiZqL+z4pHEyXoS0F8RDx5GXm4BrqSEJFAr0m9lJAvBTaVbMjYklFseUDPAABi98aWql9TC1M6T+jMyhdXcn77eWr1q0WPiT2wq6p/urp24hrx4fFkxGeQEZ9BZkImmfGZXAu/RnJMMpaOlgxfP5y277fFNdCVda+vY+/Pe6nZt6Y+/v0Pu0mISKDf9H5YuxYNzaBQPOwoQV8K4vbFAdDouUZsfHcj8Sfi1cZrKbBxtyErKQttrhaNuaZIuaWjJRoLDUlRSaXuu/ELjfFo7oHUSSo1qIQQghPzT7Dm9TVkXssstp2VixUZVzOY2Xkmb196m1p9axHQM4D0K+nYe9ojpWT/H/vZ8NYG/mr4Fy/sfQF7D6XDVzxaKEFfCq4cvgKAfzd/Nr67kbNbzypBXwoqNagEwPkd5/Hr6Ge0jl1VO66fv353/dfX9y+lZPGwxYTPCQcBXsFeeLbwxNrVGht3G2wr2WJXzQ7Xmq5ozDQsGLSAiMURjLcbj4mZCflZ+ei0OizsLKjUsBKNn2/MU2ufYmbHmRybdYy277W9uwugUJQTStCXAkdfRwCuHr+Kay1XLuy4AB+U75geJXxCfACI2RhTrKB3q+NGytkU4iPiDUHRSoOUksktJ3Np3yWsXa15btdzuNRwuW2bQQsGseyZZZxecRphIrDzsMPMyozrF69zYfsFLmy/YKi7//f9tHyzpdFctQrFw4q6W+9AZkImF8IuELM5hjPrzwBwZt0ZTExNlH6+lFg6WBI4MJD9E/fTZlwbrJyLehF7B3sTtSqK0ytP35Wg3/LxFi7tu4RrbVdeOvQSphamSCmJXBbJ9fPXMTE1wcTMRG++KSE1LpXU2FTqDKnDgJkDCvV16cAllgxfQuKpRMOx1NhUjk4/SpNRRVIxKBQPLUrQ30RmYibxJ+OJ3RPL+dDzXDl6hbS4NADMrM3wDvametfqnN10loTIBAIHBpbziB89Gj3XiIjFEVw+dBm/TkVX9TX71GTTuE2c336eNuPalLr/8DnhYAKjDowyrLovH7zMggELbtvu7Kaz1Oytj5qZGpvKxnEbCZ8bjrWbNV1/6krdp+pyatkprhy9csegawrFw0ZJcsZaAtsBi4L6i6SUnwkhGgJ/AZbo0wS+KqXcZ6S9I/rE4XUBCTwnpdx9vyZwP0iOSWbDOxuIXBppOOZayxXf9r5UalAJj+YeuNR04ci/R9j9w260eVp6TOyhVnV3gVdbL8xtzTk265hRQe9a0xWhEcSfiC9132dDz3L94nWsHK0ws/ov/nyl+pWwdLSkSuMqDJw3EF2eDm2e3gPXtrItO77ewfavtrN0xFIc/RzZ8+MedPk6gj4Kos24NoawDOr/rXhUKcmKPgfoIKVMF0KYAWFCiLXA/wFfSCnXCiF6ABOAECPtfwHWSSkHCSHMgYfGPi0nLYew8WHs/mE3JqYmtHizBdW7VMfE1ISIJRGc3XSWqDX6nOe5Gbloc7RUa12NPlP7lDg1nqIwFvYWeLX14trx4u3prV2sSbucVuI+87LymNJ6ClePXAWg8ajC9vMacw31R9TnwF8HyEnNwbm6c6HyhiMbknI2hai1UWQlZlG9S3V6/tkTJz+nUsxMoXh4KUnOWAmkF3w0K3jJgtcNOzMH4NKtbYUQ9kAw8GxBX7lAydINlSFSJzk26xib3t9E+uV06o+oT8fxHbH3sOfslrPM7jEbE1MT/Dr5YV9NP0WNuYaGzzQ0WHYo7p7E04m3vY7OAc5cDLtIZlIm1s63XxfkZuYysdZEUi+m4hroSv+Z/anapGjUybYftOXw1MNsem8TQxYNKVTm5OtE/5n90Wl1pMam4uDloPZfFBWKEunohRAa4CDgD0yUUu4VQrwJrBdCfI8+lEJrI039gHjgXyFEg4I+3pBSFvGaEUKMAkYBeHl53cVUSs7Kl1ZyePJhPJp78MSSJwwmklePXWV+//k4+zvzzJZnsHG3KdNxPI5kp2STHJNMk5eKV4N4NvfkYthFolZFFRsADSArJYs/av9B+uV0qnerzvC1w43WkzrJmQ1nMLUw5fTK08Xa8ZtoTHD0diz1nBSKh50SxbqRUmqllA0BT6C5EKIu8AowVkpZDRgLTDHS1BRoDPwppWwEZADvF3OOSVLKplLKpm5upbe2KClZyVkcm3GMhs815PndzxuE/PWL15ndYzbmduYMWztMCfkyIvt6NsBtPUz9e/gDELM5ptg6KedT+NX3V9IvpxPQJ4Bha4YZP19KNv8G/cvyZ5frf8BDnzEq5BWKikyprG6klClCiFCgG/AM8EZB0UL0G663EgvESin3FnxeRDGC/kFxcuFJtLlamo9ubohdkp2Szezus8lNy2Vk2EgcqqkIlGVFbrpec2duW3wWqRsRJ8PnhJNyLgVtrla/gXrjb76WlJgUpE7S9OWm9Pyzp9F+Tq86zaqXVpFxLYO+0/rSYEQDFa9G8VhSEqsbNyCvQMhbAZ2Ab9Hr5NsBoUAHIOrWtlLKK0KIi0KImlLKU0BH4OR9HH+pOTbrGK6BrlRuVBmA/Jx85vefT+LpRIavG06lekoHX5bcEPRmNmbF1rkRoliXryvkrASA0EegNLc1p9uv3Wj4TEOjfRyZdoTlI5dTqX4levzRgxrdayghr3hsKcmKvgowvUBPbwIskFKuEkKkAL8IIUyBbAr060KIqsBkKWWPgvZjgNkFFjcxwMj7PIcSc2TaES7suEC7z9qRcTWDVS+tIj4inqSoJPrP6o9vB9/yGtpjww3b9vzs/NvWc6nlQnhkOMdaHcPU1BRTU1MmT56MlJIhQ4Zw6tQp2lb/LxTBm2++yZ49ewDo168fDa82BOCnUz/xTb9v0JhriBNx7N+/n3r16pXN5BSKh5SSWN0cAxoZOR4GFNlRk1JeAnrc9PkI0PSeRnmP5GXlsen9Tez7VW/m793Om+ntp3P9wnW8grxo814b6g+rX55DfGywqaTf+0i/kn7beh7NPTgXeY6/P/yber3qsWbNGj777DP+/vtvNm7cyFtvvVWo/ujRo/n555/R6XS0adOG7j90JyA6gM/F53g092DbpG1MvDSRAO+AMpubQvGw8lh4xq58cSXHZx+n6StNCf4kmBkdZ3D9wnWGrR1220QYivtPytkUABy8br8P4tvBl2MzjnF522Xq9aqHubk5pqamWFtbY21ddCO3Ro0aAJiYmKDRaHCo6sCTK580lG9O2EztX2pzdMZRmr/W/P5NSKF4BKjwGaZ0Wh3Ra6MJ6B1Azz96cnHXRRIiEuj7b18l5MuBq8f0Tk138kcI6K1fecftjSMjI4OPPvqId9999479z5w5k+rVq+Pj41Po+NL1S6lHPaPxdRSKik6FX9GfWn6KrKQsAgcGErE0gmVPL8PRx5EaPWuU99AeS7JT9OaVGrPbmzhaO1ujsdBw7dQ1nnjiCT744ANq1759jJn1a9Yz5e8prFy1stDxiIgITPJMcMYZj+Ye9zYBheIRpEKv6HPSclgwcAE27jZUa12NJcOW4F7Xned2Poe5TfHmfYqy40ZAsKMzj96xrnUla2bGz6Rfv37069fvtnXXzlnLqL6jaLmzJSuHFxb0M2fOJNg3GI25xpB1SqF4nKjQgv5GxEK/Tn4c+OsAujwdQxYPUV/2csTZ3xnvYG+OTD2CPrpG8Zx3Ps9peZpp/0wjJCSEMWPGkJqaSqdOndiwYQNjx47ls88+A+CV118hOz+bheYL+XL3lxw8eBDQx6dftGgRwTWDAZSJpeKxpMKqbs7vOE/Mphj8u/vTa1IvfvL8icABgdh7qjRw5U3D5xqy/NnlXNx5Ea+2xYe7GDRkEM5HnOk0sFOhkMWbNm0qUnfGmzPY+slWbF1s8e3gS5MmeoOw3LRcln69lKPTjqLN1XL50GWqta52/yelUDzEVMgV/blt51g0ZBEOXg4Mmj+I8HnhZKdk02x0s/IemgK9+sbc1pyDfx+8bb0bJq+7vt91x9W/g7feiif9cjon5p/ga6uv+bvx33zn9h2LhiziXOg5/Dr74ejjeF/moFA8SlQ4QZ9yPoWZnWdibmvOU2uewtzWnP0T9+NWxw2voLINlqYoGeY25jR5qQnHZh3j9KrTxdZz8HLAv4c/mfGZ7Pxm5237rDOkjuG9o48jjV9sjIWdBU1fbcqz25/l/evvM2LDCKW2UzyWVDjVzZ6f9yB1kqc3P42DlwOxe2K5cvgKPf7ooULPPkR0+KoDZzefZdkzy3jpyEvFxhcaMHsA37t/z5ZPthDQNwD32u5G65lamOLfzZ/4iHhGR47GRFPh1jAKxV1Tob4N+Tn57P15L4H9Aw0OOTc8MO/kcq94sJhamjJowSC0eVrm9Z1XbKIRK0cr+k3rh9RK/m37L/k5xf8fnWs4k3E1A12+rqyGrVA8klQoQX/DGSdw0H+5XGv2qYmNuw1h48PQaZUAeJhwqeHCoHmDSDyVyO81f2fvb3uN6uLrPVWPhiMbkp2czfT204vtr0rjKuRn5xO3L64sh61QPHJUKEGfcU2fz+TmDTdhIjC1NMXaxVqZ1j2E1OhRgxcPvIhXGy/Wvb6OdW+uM/qD3HdqX1xquhC7O5bNH2022letfrVw8HJg+cjlhrj3CoWiggn6zPhMAGzcCicNqd61OolRiXeVcFpR9rgFuvHU6qdoObYl+37dx5Knlhhd2T+/53nMrM0I+1+Y0aQklo6W9J/Vn+vnrzOvzzzysvIexPAVihKRlZRFxNIIIpZE6P8ujSByWSSRyyOJXBZJ+LxwLh+6XCbnrlCbsRnx+hW9tVvhoFfBHwdz6J9DHJp8iG4/d3vg49JpdWpz8A4IE0HXH7uCgD0/7qH24NoGL9obWDlaMWzdMKa1m8bcXnN58+Kb2LgW/lH3DvKm34x+LBm2hJmdZ/LkiidVfBvFQ0HoF6GGCLrF4eTnxOtnXr/v565Qgv76hetA0exFN1aHt/4AlDVnt5wl7Jswzm4+i5m1GdXaVGPIoiG3za70uBPyWQjRa6NZOHghjV5oRJfvu2DpYGko9w7yJuSLEEI/DWVKiymMiR5TxJqq3pP1MNGYsHTEUub0msNzO59TFleKcif5TDLmduaM3FGQkkMWyKaCh9d5/eZhYW9RJueuMMvM7JRs9v++H6DIl9qhmgOerTzZ/uV2EqMSy3wsGdcymD9gPjM6ziAhIoGWY1tS54k6nNlwhs0fGtcvK/RY2Fsw6uAoWr/bmsOTD7NkWFE1TrtP2uHdzpvkmGSWDFtitJ86Q+rQ88+exO6OJWJJxIMYukJxWxJPJeLf1Z/KDSrrXw0rU6VRFao01r9MTE1wruFcJueuMILezLr41HTCRODZ0hNtjtaQyq6siNkcw18N/iJqTRQd/teBMVFj6PJ9F/pM7kPTl5uy/4/9xJ9UewW3w8zKjM4TOtPqnVZErY5i1UuryM0o/H8bsXEE1q7WhM8N5/C0w0b7afBMA9xqu7H6ldXE7ol9EENXKIyizdWSfDYZ5wDjgjw3PZeUsylUbli5TM5/R0EvhLAUQuwTQhwVQpwQQnxRcLyhEGKPEOKIEOKAEKLYbA5CCI0Q4rAQYtX9HPzNaMw1tP2gLSamJqTGphYqy07JZv/E/dQbVo8qjaqUyfmlTrL5o8182+lbJqdOZm29taxMXknYnjCqVKlCSEgI3x79lnireEI/DyUpKYlevXoRFBTEmDFj7uji/zjS+Vu9sD885TDTQ6YXsqTRmGl4btdzCI1g5QsrSTiVUKS9icaEIUuGYG5jzr9B/xK7Vwl7RfmQcCoBqZW41zHu8JcQqb9/XWq6lMn5S7KizwE6SCkbAA2BbkKIlsAE4AspZUPg04LPxfEGUObPz01GNUGn1XHwn8IxVPIy89Dmau+Y1ehukTrJjvE7CP1fKHur7iXsTBg79+9kwgT9JenZsyehoaFs37md7s90J2p1FF9//jVPPPEEO3bsICMjg/Xr15fJ2B5lhImgy3ddGLJkCJcOXGLbF9sKlbvUcKHP5D5IrWRqm6lGnalca7oy6uAoLJ0s2fPTngc1dIWiEJcP6q1piluxXzl6RV/eoGxW9CXJGSuBGwk+zQpesuB1IxSkA3DJWHshhCfQE/gaeMtYnfuFo48jfp38ODz5MEEfBhkSUdtVtSNwQCD7ft9Hq7dbYe1yb5uyS4YtIWbTf+Z9+dn55KTmoAnS4Oviy7MvPkv8uXj6VOtDTkYOa0+vJSgoiIYNG/Luc+9y6J9DrJy9ko+/+BiA3r17s337drp1e/AWQY8CtfrWovGoxuz5aQ/u9dxpNPK/FMYNn23ImQ1nCJ8bzswuMxm5rWjueStnKzyaeRhWTQrFgyZmYwxWLla41nI1Wh67JxYLBwuc/JzK5Pwl0tEXqF6OANeAjVLKvcCbwHdCiIvA98AHxTT/GRgH3NYtVQgxqkAFdCA+/u512PWH1yftUhqX9hf+3Wn/ZXty03M58OeBu+4b4OA/Bzk+5zhVm1al1oBa1BpQi3rD6zFo/iBqvFiDo0eP8l6P92gT3oaf1/+MdpeWV3NfZcYXM7C3t2f66ukEfxpMUlIS6af0v5+Ojo4kJpb9JvGjTM8/euLbwZeVL6xk6dNLOTb7GFnJWQAMmDUAJz8nLmy/UGTVf4P8nHyVbEZRLkidJGpNFDV71zTqtCmlJGZjDL4dfMvMqbNEgl5KqS1Q0XgCzYUQdYFXgLFSymrAWGDKre2EEL2Aa1LK28ej1Z9jkpSyqZSyqZubW2nmUAj/7v4AXNh5odBxt9puuNZyvWv3+NTYVFa9vIpVo1bh29GXIYuH0OvPXvT6sxc9J/akzpA6uLq70rh2Y3Z+tJM6retQo2UNhu8YjmMlR2Z2nkk9WY8DBw7Q7JVmWGLJkaVHALh+/TrOzmWz215RMNGY8MTSJ2j6alNOLT/F0uFLWThoIaBX8Ty3+zk0lhpCvwgl7ZKRuDkSks8mo83TGg6dPHmSkJAQQkJCaNWqFS4u/+lHp06dipnZfxv8Wq2Wd955h06dOhESEsLJkyfLbrKKCsWVI1fITsnGp72P0fLkM8lcP38dv05+ZTaGUlndSClTgFCgG/AMcMO2bSFgbDO2DdBHCHEOmAd0EELMusuxlggbNxvcarsRuSQSqSu8wenbwZfTq07rY6roSrb5ef3idRYOXshP1X7i4N8HaT2uNcPXDcfUsqjWq4ZLDfat34eJpQmd/upEfHw8VQKr8OK+F6nZpyYzxs9Ac06DlbMVDbwasHjmYgDWrFlDu3bt7n3yFRwLewt6/NaDdp/pr5V9tf+SyNi62xLyaQhI2P/H/iJtG49qTPrldOb3n284Vrt2bUJDQwkNDWXs2LEMHjwYgOzsbJYsWUK1av8lKJk0aRIBAQFs2rSJ0NDQO+avVShucGLBCYRGGBaht3JDP+/ZyrPMxlASqxs3IYRjwXsroBMQiV4nf0M6dQCibm0rpfxASukppfQBhgJbpJTD78/Qi6dW/1rE7YtjVrdZheKmdPq2E/5d/Vn3+jqmtJrCgb8OFDHbu0Ha5TTWvbmO3wN+5/Sq0wR9HETH8R2x97Rn1/e7WPv6WhYNXcSCQQvY+ulW1r+1nvkd5tPasjWLqixi8AuD+fbbb5k9ezatQ1ozMXEiZzlLwLEAcjNyeabbM+y+upugoCDMzMzo0qVLWV+WCsPBvw9SqX4lek/qXeh4oxf0uvszG88UaVNnSB2avNyEqNVRbP5wcxEz21mzZjF8uP7W/PXXX3n55ZcxMfnv67Fw4ULOnz9P+/btee2118jNLVszXUXF4cyGM3gHexcJzXKDvEx9qI4be4plQUl6rgJMF0Jo0P8wLJBSrhJCpAC/CCFMgWxgFIAQoiowWUrZo4zGfEfa/197rJyt2PD2Brb93zbaf9Ee0Ce8eGrNUxyZdoSw8WGsfmU127/aTqdvO1HvqXpkJmQSszGG6HXRnFx4Em2elgYjGhD8aTDH5xxn8wf/OTtZ2FtgU8kGIQSRSyMxMTUhoHcAL094uciGyquvvgrAxNoTsfewx9zGHBtTG57SPcXbC9/GtrLtg7s4FQDXQFfObT1X5KnMxs0Gc1tzEiKKbroKIej8bWcu7LhA2PgwDk85TIevO9D4hcYkJiYSGRlJmzZtSE5OZvv27YwbN44333zT0D4uLo4qVaqwdetW3nnnHaZOncrLL79c1lNVPOKkXU7jyuErtP+qfbF1Tq88jZWLFU7Vy2YjFkpmdXMMaGTkeBjQxMjxS0ARIS+lDEWv9ilzhImg5diWxO6OZfcPu2kzro1hI04IQaORjWj4bEMuhF1g/dj1LB2+lOUjl6PL06/+rZytqDe8Hm3fb4tzdWf2/LKHrR9vpd5T9ej6U1fMbc0LOWjlZ+ej0+ruuNknTATmdvo6Ld9syaF/DrHlky30+adPGV2Jikduei7xJ+IxszZDaIpuXLnWcuXSgUtkJWdh5VQ4xo2FvQWvhr9K7J5YNr23iZUvrtTH1snZw+DBgxFCMH78eMaNG1ekX2dnZ4NVVLdu3ViyxLhHrkJxMycX6fdyavWrZbQ8MyGTyGWRNBvdrExX9BXGM/ZWhBD4dvIlLyOP89vOGy33DvLmxX0v0n9Wf1qObUnHbzry/J7nGRM1htZvtyZqdRRzes5h/ZvrCRwQSN9pfbFxtynihWtqaXpHIS+lJPViqiGVnUsNFwJ6BXBi/on7N+kKjpSS5SOXkxyTTP+Z/dGYaYrU8e3sC8DxOceN9pGZmMnBvw8aTC0P/HmA2bNnG9Q2p0+f5n//+x/dunXj8uXLPPHEEwCEhIRw4IDeYuvAgQP4+xvXtyoUN9Bpdez9ZS9Vm1Yt1lHq6Myj6PJ0NH6+cZmOpUIFNbuVek/WY8tHW1j27DLaf9mehs80LLSJmpeVx7nQc1zcdZGLOy+ScTWDLR9tQWr/UwnYuNvQ/qv2tH1P73V7t+Rn6W3tzWz++5EwtTQlNy1XnyavZwCeLctuM6YisPvH3ZxcdJLO33Uu1kKhwYgG7By/k9OrTtN8dFH7gH2/7+PItCPUG1YPv85+mNY2ZdIrkwgM1CerWbZsmaGuv78/8+frN2/HjRvHyJEj+euvv3B2dmbmzJn3f4KKCkXE4giSzyTTaVEno+VSSg5POYxHCw/c6xr/IbhfVGhBb2FvwbPbnmXF8ytY/fJq1oxeg4OXA3ZV7dDmaLl67CraXC1m1mZ4BXlRtVlVbNxssHa1xsLBAt/2vvfNgcHETP8jkRSVZDjm19mPMxvOEPa/MMLGh9HouUbU6lcL346+ZfoY9ygSHxHP5vc3U6t/LVq93YoLYRe4fv76fxWE/iWEQGgElw4Y9d/Dror+iarVW62o0lgfDuPGSv1WoqOjDe+dnJwK/QgoFMWhzdWSl5nH7h924xroWqzaJm5fHPEn4uk1qVeZj6nCSxP3Ou48v/t5zoWe4+yWsySfSSb9SjpmVmY0f705fh398AnxMWoueT8J/SwU0KfFu0GjkY1oNLIROak5rHtjHeFzwzn0zyHsq9nT+bvO1H2ibpmO6VFi8web0Wl1ZF/PZrzteIOlQnFkJWShzdMWUe/UHlybta+vZe8ve+kztY/KE6C4ryRFJ/Fngz/Jz9SH4wj5IqTYe+zwlMOYWZs9kO95hRf0UKCvb++Lb3vfcjn/mQ1n9Cv2FxoVSaYB+iePvv/2peefPYnZFEPo56EsHroYc1tzAnoGlMOIHy5i98RyavkpAM5tOQdCnzWsapOqwH/5Bm78PbPhDFcOXSFyWSR1Btcp1JeVkxWNnmvEgT8PcOngJTqO70hArwAVr15xzySeTmRS00kGIQ/Q4OkGxdY/vfI0NfvWLLMY9DfzWAj68mbdm+sA6P5L99vWM7U0JaBXAH6d/Pin2T+sH7teCXr0Vk2eLT2xrWrLtfBrJJ1OIm5vHO513Qn5PKRIIpeavWsytc1UwueFFxH0AD0m9sC3gy+b3t/EvD7zcKvtRsu3WlJ/WP0yf7JTVEyklCwYuIDcNL1/RbNXm9Htt26FfDFuJjMhk/Qr6VSqX+mBjE89tz4APJp5ADB/wHzSLhtxz78FU0tTXAJc7mnztyLhE+LD87uf54nFT/Ba5Gs8u/1Z/Lv5s+enPUxuMdmQFP4Gnq08ERpB7C7jYYmFENQeVJvRJ0fTf2Z/TMxMWPnCSn7x+4XD/x4usde0QnGDc6HnuBZ+DdB7bPeY2KNYIS+lZON7G0FAQK8Hs5BTkuQB0Puf3rT7vB3nt59nevvpZCZk3rFN2uU0w8ah4j9umMUOmDMA72Bv4k/Gc3br2SJ13Gq7kX4lnUsHjW/Kgj6HQf3h9Xnp8EuM2DQCR29HVjy3grBvwsp6GooKxLXwaywcvNDwufuvxT+5a/O0bHhnA0emHqHtB23L3NrmBkrQPwA05hpCPgth2JphJEUlsWTYErS52tu2cfR25OyWs/zs/TNze88len20Sk5SgE6rY9nTyzgXeo46T9ShVt+iVg03vmzLnll2x/6EEPh19OO5nc9Rq38tdny9g9S41Du2UygAFg5ZiC5f72xp7WZdrJUN6EOc7/lxD81GN6PDlx0e1BCVoH+Q+IT40PPPnpzZcIZp7aYRPj+cuP3Go2l2/akrbd5vg3c7by4duMTsbrOZHjKd8zvOF4rA+Diy5+c9HJt1jJAvQhg4d6BRvbpPiA+utV2JPxHP+R1FHeaMIUwEXX7ogk6rY9N7m+7zqBUVFgk513MAaDa6meFw165dcXNz46uvvgJg39p9vLHwDb4x+wb7ofaGkMTGss1lZWXRuXNn2rZtS8uWLVm7du09DVEJ+gdMk1FNGDR/ENdOXGPx0MVMbj6ZbV9uK7Jat61sS6fxneg/oz9vnn+Tnn/1JCEygWnB0/jG4Rtmdp75WKbGS41LZctHW6jVrxbBnwTf1lqm37R+AKx4fkWJ+3fydaLV2604Pvt4sT/CCsUNEk8nkhSj940xMTUh+KNgQ9mUKVP47rvvDJ/Pzz/PSMuRDBw4sFAfEyZMKJJtztTUlH/++YewsDBWrVpVKO7S3aAEfTlQZ0gdXj/zOi/sfYEGTzcg9NNQVr+yulCkzZvRmGto+lJTxkSNYeC8gTQZ1YT4k/FMbTOVXd/veqxUOnH74tDmaGn7Qds7mkR6NPOgcqPKJEUlEb0++rZ1b6bte22xqWTD0hFLC+WpVShuRuokK0etRJer/94G9AkoZEDh6fmfp3v61XROzT1F65GtizhDhoaG0quX3mnqRrY5MzMzfHx8ALC0tCx2Y7ekKEFfTti42eDR3IO+0/rS5v02HPz74B2zX1nYW1D3ibp0+7kboyNGU6tfLTa+u5GFgxYasi1VdG7YHJdUAPef0R+AVS+VPC+9hb0Fg+YPIvlMMkueWlLsD7Di8Wbf7/v0cbQK1hvdfi4+Fej+P/ajzdPS8s2WRcqSk5NxdHQEjGebe+ONN4wG2isNStCXM0IIOo3vhGugKycWlDzAmYW9BYMXDqbz952JXB7Jj1V/ZHqH6ax5bQ2Lhi5i5Usr7+g9+iji0cwDE1OTQjl7b4d7XXc8W3ly/fx1Ti4ueVYon3Y+dP+tO1Frolj35rrH6qnpcSftUhr7ft/Hv8H/8me9P43mrEiOSWbdG3r/GCQ413DGoZqD0f7ys/PZP3E/Ab0CcAlwKVLu5OTE9ev6cB63Zpv78ssvcXJyYuTIormQS4MS9A8JDZ5uwIUdF0g5n1LiNkIIWr/dmlEHRhlitB/48wCX9l/i0D+HWDR00R2tex41LOwt8O3gS+TSyBIL3/4z9av6tWNKt6HV9OWmtHqnFft/38+K51eQfjW9SJ3cjFz1I1CBiFgSwU9eP7F2zFou7LjAtfBrRfK4ZiZmMrv7bACDw1P7L4uPN39u2zmykrII+TzEaHm7du1Ys2YNUDjb3O+//05UVFQhPf/dotwAHxJumPPlZ+XfoWZRKjesTPoVvRAafWo0Lv4u7P9zP2teXcOMTjPo+mNXqjatarTt5cOXOT7nOI7ejuRl5ZFwMgGNhQZLJ0usnK2wrWyLf1d/bNyNZ8cpD2r1r8XqV1Zz7fi1EnkWOld3xqe9D+e2nuPojKO3dUu/lc4TOmNiasKu73ZxYv4JfDv6UrVZVcyszDiz/gwxm2Jo/1X7QptwikeP43OPs/3L7SRFJ2FhZ4FdVTviT8bjE+KDmdV/EWfzsvKY13ceKedTGLJ4CAsGLcCmko3ReDUvvvgiYdvCuBZ1Df9q/oz1H0unTp04efIkJ06coEePHnzxxReMGzeOp59+mj///JP69evTpUsXrl27xhtvvEGrVq1o317/I7J582Y0mqKhuUuCEvQPCZf26x17HH0cS9326KyjpF9OxzvEGxd//aNhs1eaYWFnwbo31vFPs3/w7+5P7UG1sXS0xNnfGfe67kidZOmIpcSfiDf0ZVvZFqmTZCVlGWyDhUbg18mPukPrYlvFFl2+DkcfR1xruZZLULDag2qz+pXVzO09lzfOvVGiODX9Z/TnJ6+f2PD2hlIJ+huqtYbPNmTPT3s4u/ksp1eeBjBkBMpKfDz2RyoyO77aQUJEArUH1ybowyCsnK34xe+XQpZXUidZ9vQyLu66yKD5gzg0+RBI6PhNR6N9/vPPPyx9eiknL55kzK4x2Nvbs2lTUbNdFxcXVq9eXeiYu7s7Wu39exq/o6AXQlgC2wGLgvqLpJSfCSEaAn8BlkA+8KqUct8tbasBM4DKgA6YJKX85b6NvgLR4esOzOw0k90/7ibow6BStd3w1gYQ0H96/0LH6w+vT80+Ndn3+z72/b6P6LX/WZ40e60Z7nXdiT8Rz+CFg/EK8kJjpk9cDno37byMPJKikzix4AThc8NZPnJ5of5t3G2oNaAWdYbUwTvY+4EJfWtXa4I+DmLHVzs48OcBmr3a7I5t7D3t8e/uT/SaaPb/sb9EbW7GtaYrvf7SW0bkZ+eTn52PhYMF4+3GGzbjFI8mV49fJTUuFXM7c7r93M2QHMgt0M3wHmDDuxs4uegkXX7oQvWu1Vk8dDGWzpY0erZIAj4AUmNTCZ8bTrPRzbD3tDda50FRkhV9DtBBSpkuhDADwoQQa4H/A76QUq4VQvQAJgAht7TNB96WUh4SQtgBB4UQG6WUJd8Ve0zw6+hn8Mp08HKg7pN1SyQ4D/x9gMz4TKp3rY6DV9HNIAt7C4I+DKLt+21JOJVAfnY+O7/Zyf7f9yNMBF5tvQgcGFhkVSyEwNzWnMoNK1O5YWU6fN2Bq8eukpeRhzARJJ5OJGp1FMdmHOPgXwexqWRD+/9rT+MXGhfRaZYF7b9oz5XDV1j3xjrc67njHeR9xzb9pvXjh8o/sPmjzTR9peldR6w0tTQ1OGmZ25oXSTSueHRIv5LOqlGryLmew8gdIwsJdqfqTsRsiiE1NpWIJRHs+XEPzcc0p+XYlqx8cSVSJ2kzrk2xfe/9dS9SJ41a2jxo7ihJpJ4bu1BmBS9Z8LrxM+UAFAkqIqW8LKU8VPA+DYgAPO7DuCsk3X7uhrO/M0tHLOVHjx+Z2WUmy0cu5/x2456dUkp9wnIB/ab3u23fwkTgFuhGlUZVCP5Ur0+WOknn7zuXSOAJIajcoDLVWlfDs6UnDZ5uwKD5g3jn2jsMWjAI15qurHppFTM6ziApOumO/d0rwkQwYNYAHH0dWThoIamxdw5ZYOOmfwLJSclh13e77ss4LOwtDF6RikcLnVbH7zV/J3ZPLB7NPfBq61WovNXbrcjLyGP7V9tZ9+Y6avWrRdefugIQPjccjbmG1u+2Ntp3TloOBycdpPag2neljr3flOhZWwihEUIcAa4BG6WUe4E3ge+EEBeB74EP7tCHD/ok43uLKR8lhDgghDgQHx9vrEqFx8HLgZcOv8SQxUPw6+RHTmoOkcsjmdZuGmtfX1vEzGvPL3vITs6mZu+a2FayLfF53Ou4M2LjCHr93QvPFveWvtDcxpw6g+vwTOgz9P6nN5cPX+bPen+yc8LOMo8CaeloydBlQ8nLzGPBwAXkZ995I7vvlL4IjWDb/21Dp7t3+3iXGi5cOnBJWd48gphoTMjNyMXKxYqRO4qaL6Zd0keaPfj3QTyaezBg9gBMNCYcmnKIvMw8avarWawj0+Eph8m5nkOrt1uV6RxKSokEvZRSK6VsCHgCzYUQdYFXgLFSymrAWGBKce2FELbAYuBNKaXRpZeUcpKUsqmUsqmbm1spp1FxECaCwAGBDJg1gBf2vMDYi2Np/npz9v22j4m1JhK1NgrQr8ZDPw1FmAj6TO1T6vP4dfKjyagm92/cQtD4hcaMPjka/+7+bHpvE8tHLi9zAehW241+0/sRty+O1aNX3/F8FvYW1HuqHnkZeWz7fNs9n7/O0Dokn0lmz8977rkvxYMlPycfqZVkJWYZDWLnXN0Zu6p2VG5UmSdXPomZtd76ZtcE/dNgt5+MO0hJnWTfb/vwauuFR/OHQ4FRqt0zKWUKEAp0A54BlhQULQSKZmIGCvT6i4HZUsolxuooisfcxpzuv3RnZNhILB0tmdNjDls+2ULoF6HkpuUSOCgQaxfr8h6mAbuqdgxZPISgj4M4OuMo0etKHnrgbgkcEEjQR0EcmXqEA3/d3rsYoNffvTAx05tM3rAsulvqD6+PW203TswvubOb4uHg0ORDgN5qxsm3aG7oqk2r8lbcW7x06CVs3PTmxelX0kmKSsIlwKWQPv9mYjbHkByTXCjAWXlTEqsbNyBPSpkihLACOgHfotfJt0Mv+DsAUUbaCvQr/Qgp5Y/3cdyPHV5tvHhx/4usfnU1O77aYTieeiGVqLVR+LTzMaw4MhMyidsfx6X9l8hMyESbq8XB2wErJyukTuJWxw0nPyds3GzKJKOSEIJ2n7TjyNQjHPn3CDW617jv57iVkC9CuHL4CmvHrMXew56afWoWW9fMyoxGzzfi4F8H2fTeJrr80OWuzyuEwKaSDbo8FSbhUSInLYe1Y9Zi4WBBi9dbGI6fPHmSV199VV8nJ4fTp0+zZMkSPvzwQ0xNTUmOSqYTnejxRg9Onz7NqFGjAGjcuDE//PADQggOTTqElYsVtfoXH674QSPu9KgrhKgPTAc06J8AFkgp/08I0Rb4Bf2PRTZ688qDQoiqwGQpZY+COjuA4+jNKwE+lFKuud05mzZtKg8cuPPK7HHl7JazzOo2C12eDjNrM/Iy89BYaLD3tCc7OZuspAK7bgGWDpaYmJoUm+zE3NYcazdrbNxtsHGzwaaSDY4+jjj5OeHk54SjryM27jZ3ZaEyu8dsks8kMzpy9APJyZqTlsOMjjO4euwqw9YOu22O4PzcfL6x+waA99Pex9T87n/wpradiqmFKU9vfvqu+1A8eL51/pbs5GwavdAIR29HWrzeolD+1gULFrBlyxZ+/fVXzM316SoHWQ/iSs4VtuduZ8DAAbz//vu0bNmSV155hQEDBtCqfit+8vyJFm+0oMv3d7+AuBuEEAellE2Nld3x7pZSHkO/iXrr8TCgiJJXSnkJ6HFTHWVlfJ/x7eDLJ7mfAJCbnsuFnReI2RRD+uV0zO3McfZ3pkrjKlRtWhULO/2Nm5OWY8hneeXoFVIvppIRn0FmfCaZ8ZlkxGeQGpvKpQOXDF62NzCzNvtP8Ps5Ym5rjpWzFR7NPXAJcMHa1dqoIK89qDYrnl9B9LroB7Kqt7CzYNjaYUxrN435/eYzJnqM4ZH7VkzNTWk2uhl7ftrD+jfX0/OPnnd9Xm2u1nCdFQ83UkoiFkcQvS6a7GR9YLzDkw8DUK1NtUKLg1mzZjFu3DiDkD8fdp70rHRqB9bGRGPC6dOnadpUL1ebN2/O1q1bsTpkhS5fR+MXGz/gmd0e5Rn7iGNua45/V3/8u/rftp6FnYVBGBWnW7xBXlYeKedSSI5JNrxSYvSfYzbHkJdRNFjax7kfozEr7J5df3h9tn2xjc0fbKZ65+p3nQO3a9euHDp0iDfeeIOPP/4YKSWvv/46R44cwcHBgRkzZuDs7ExSUhJPP/M0CRYJyFRJw/EN6fZjN1asWMHXX3+Nubk5L7/8MsOGDQP04Q0O/HmAQ5MP0eWHLoVc3UuDNleLxuLuXNMVD5YT80+w+MnFmFqZYu9pbzDLtXKxMuR2BkhMTCQyMpI2bfR28qtXr+a1J18jlVQ++ERvYFivXj3WrVtHz549WbduHY6OjhxddhSvtl641nR98JO7DUrQK4pgZmWGW6AbboFFrZ+klEitJDU2lStHrzC/33wAo0JcY66hy49dWDhoIfsm7qPlGy25fOgyaZfT8A72LvEqeMqUKWzatInYWH2ilfXr15OZmcmOHTuYMWMGEyZM4JtvvjEkcBgxYgQd/Tsy9/e5BH0YxLvvvsuBAwewtLQkODiYnj174ujoiImpCa3ebsWOr3ew+tXV9Pu3311dL22utsiPnOLh5PDUwzj7O/PqyVfRmGlIu5yGNleLbSXbQvtV8+fPZ/DgwYYn1R7de/B8zvOcsDzBxKUTafdkO3744QfGjBnDzz//jL+/P3bCjoSIBHr93au8plcsKnqlolQIITAxNcHRx5FafWvR8q2WmJiaGB6DbyVwQKDB3PLf4H+Z1GQSc3vN5Xv37wmfF16ic96cwAGMJ2q4+biUkobODYnJi+HalWu4ublhZ2eHmZkZAQEB7Nv3X6SOkP8LwczGjGMzj5GTdneOT2ZWZuRlVbyQ0BWNtMtpXNx5Efe67oYfZrsqdjh6OxYxSpg9ezbDhw8HIDs7mxMLTqDN1eLfwh9ra72Vm6enJ0uXLmXjxo1kZGTgn+mPxlxD7cG1H+zESoBa0SvuiXpP1WPPj3uIXBZJo+eKxvwQQtB/Rn+WPr2Ui7su0ub9Nvh28GX7/21n6YilmNuaE9AroFTnTEpKwslJbw7n6OhIUpLeE/dGAocd/9tB4v5EnJs741PHh4SEBOLi4rCzsyMsLIzu3bsb+jIxMSHowyC2fLSFlaNWMmjuoFJfAytnq2I3u4vj8qHLLBq6iNz0XDTmhZ8G7KrY8czWZwzC50LYBUOIZJcaLiWK2KkoypaPtpCfnU+Hr2+flDsmJoacnBwCAwMBva5+wtgJ5JGHT74P076YBsCcOXP4559/EEIw7KlhpHySQo2eNbBysirrqZQaJegV90SVRlUwszbjypErxdaxdrVm2JphhY55tvBkRscZLBi4gJE7RhocS6SUJJ5KJPt6NnZV7LCvZl9ko9fZ2ZmUlBRAn6jhhtB3cnIiKT6JbZ9vw62lG5XbVUYIwaRJkxgxYgQ2NjbUq1ePqlULh2xu+0Fbwr4J4+TCk2T/lY2lg2WproF7PXf2/7GfnNScQlYbt+P6heskRSXh6OuId/B/cXqSopO4uPMiMzrNYNiaYUSvi2bRE4sM5dZu1rx77d1SjU+hJzc9F2Gij+F0O/z8/LjZ6m/4oOHEjYrDtrItb4e9bTj+1FNP8dRTTwFwasUp5l2ZV6rIqA8SpbpR3BPCRODZ0pPIZZFkJpZ8VWthb8Hw9cOxcLBg66dbkVJy/cJ1ZnScwcTAiUxpOYWfvX9mapupRVQqxSVqaNeuHWs3rMXEzISjCUcJCQkBIDg4mC1btjBnzhzS09Np0aJFof6EEAR/HIzU6nOAlpbAAYFoc7ScXnW6xG182vtg4WCBg5cD/ab1++81vR8BvQK4uOsi09pNY+WoldhVteOlIy/h28GXrKQsFW6hhNwagqPxC43R5etuuygxxqYPNoGE5mOM+oQCcHDSQWyr2Jb66fRBoVb0insm+JNgvYCuNZF+0/tRo0fJTCmtnK1o/W5rNo3bxNzec7mw4wJSJ+nyYxdcAly4cuQKWz/eSnClYC7Ki+TLfNbNXseWg1tYtWoVQUFB2NvbM2PGDABDAodz9uewO2dHly5dDMf379+Pqakp48ePx8Ki6Kq79but2fH1DiIWRZCdko2lY8lX9dVaV8PZ31lvXdS1eok8lS0dLAkcGGiIbX8D5+rOPLnySQ5PPcyen/ZQuUFlOvyvA5UbVKZKkypcCLtQ4nE9rkgp2fXdLrZ+ulUfB6qKLQ7eDpwPPY/QCDxalDwsQXZKNocnH0ZjoaHNe8YjVV6/eJ3otdG0/aDtXVuWlTVK0CvuGZ8QH0YdGsXS4UuZ03MO/t39CfooCK82Xnds2/rt1lw7do1js47hFeRFv+n9DO7oAT0DcPRxJHBJIPnZ+aRfTefywctEr41m4sSJRfq6kcBhw7sb2D9xvyHg1IQJE+44DiEEwZ8Gs/Gdjax8cSWDFw4u8fyFiWDAnAH82/ZfZnaayfANw7FxsyHs2zAcvR2pO7Ro9iEAU4viv36NnmtUZM/DzsMOba6WzITMYv0DFBC5NJJN7+kTfJxcVDgiep0hdUoVAHDhEwvR5evo+GXHYsOGH/rnEFJKGj1vPC79w8DD+fOjeOSo3KAyL+x7gU7fdiJubxz/tv2Xvxr+xenVt1dnCBNB/5n9eTfhXZ4NfbZIzJH6w+ozZPEQnlr9FAPnDAQBy0cuJ2pNkYgbhfqUOllqFUfLsS2xcLAgYkkEWSmlyxrl0cyDoSuGknAqgWnB0ziz8Qyb39/M4icX6yNl3mNMHcCQvCItLu2e+6qoXNx1kQUDFwD6vaGbMbczxyvYi/yckqXrjNsfR8yGGGyr2NL2/bZG62TEZ7Dn5z3U6lvLaLychwUl6BX3DTMrM9qMa8ObF96kx8Qe5GflM7fXXGZ0msGZjWdu29baxfqOCUtcAlx4fvfzOPo4MqfnHM5tO2e0nl1VO7Q52lKn+DMxMaHdp+2QOsnqV1bfucEt+Hf1Z/j64aTGpTK/v96/wL+7P6GfhTK1zVSuhV8z1M3Pzufyocu3XdXfir2HXtAbi7T4uHP1+FWmtZvG1DZTDccyEzJx9HU0fM5Ny2Xta2sJ/Tz0jv3pdDpmd9MnAB84b2Cx9bZ/uZ28zDw6jjeeTvBhQQl6xX3H3MacZq8245Xjr9Dlhy5cPniZ2d1nkxGfcc99e7bw5IU9L2BfzZ6N72w0GvP+xsoqPqL0eQ2avKSP6nH9/PW7Gp93kDcdv+lIXkYe3u28GbZmGIPmDyIpOok/6//JzC4zCf08lFndZhG3L67YfKPGsPPQezSrFX1RNr6z0WiCnsYv3F0ogiVPLSErKYu6T9bFJ9jHaJ2rx65y4M8DNHq+Ea61Hi5P2FtROnpFmaEx19DqrVbYedixeOhiYnfH3jaqZEkxszaj/ZftWf7sciKXRxLYP7BQuXc7b0zMTDi14lSJUgzezPlt/wmLXT/sIi8jj9yMXPIy88jLyCMvK0+fMzZLnzdWp9Xpc62B4e/lw5cB6P6r3l6/zpA6+Hb0Zf/E/RyecpiYjTGY2ZjR+5/e1B9Wv8Rjs61sC0Kt6I3R9oO2nNmgf2ocOHcgyTHJ7Jywk6PTj+LfzR9LJ0vcaruh0+poNfb2yUCi10dzYv4JrFys6D+rv9E6Oq2OFS+swNLJko7/e7hX86AEveIBULN3TZxrOLNm9Brc67nfF11m/WH12fH1DkI/C8W/m3+hODWWDpb4dfIjckkknSeULFXiDW5s3sXujiV2d+xdj8+jpQeV6lfi8OHDvPbaa2g0GkxNTZm8dTIXzl/go48/Yv2M9ZjMMmHGjBlUq1aNcePGGbx2T506xYcffsiYMWNISEhg9OjRxMfHE6uJpVHcw7vpV174hPjw0uGX+LvR36x+dTU+IT7kXM8h53oOiacTGZc4zpD4/nZkX8/Wq90EDF83vNgMUnt/2cul/ZcYOHfgQ5UPojjuGKa4PFBhiiselw9fZkbHGWjMNYzYOIJK9e7du/PkopMsHLwQp+pOeLXxQpgInKo70XJsS8LnhrPyxZW8dOQlKjeoXOI+p3eczrkt52j9bmvsPOwwtzXHws5C/9feQv9ysMDSwRJzW/M7mtNduXIFGxsb7OzsWLNmDXPnzmXyP5PRoMHU0pSpU6cSERHBd999V6hd/fr1Wbt2LR4eHgwfPpwPPviAmF9jODTpEDX71mTosqF3dc0qOrF7Ytn+5XbDZr3GXEO/Gf2o+4Rxy6db+bPBn1w7do2gj4Po8KVxD9rkmGT+qPsHfh39GLpi6AMJwV0S7ilMsUJxP6jSqArPhT3H343/5vCUw3T72XgattJQe1Btnt78NJve28T57efRaXWkTkvl4s6L9Jvej5UvriRyWWSpBH1SVBImpiZ0ntD5nscHULmy/tyXDl7i8F+HORN2hgn2E9Dl6XCq7kRC9wTqNy+svjl06BDu7u54eHig1WoJDw/nhx9+YPey3fjhx6jPRt2XsVVEXANdca/nbhD0Q5YMIaBnyZyYNn+wmWvHrlG5UeVihfwNlY2JqQk9/+z50Aj5O6EEveKB4VbbDTNrM71e+z7h28GXF/e/aPi8+tXVHJx0kJ3f7QQK9NqlIONqRhGzvHtBSsn6t9az4+cdzDCZwWutXqNxm8ZsO7CNb7Z8Q87vOfwy+hfyh+QbLHBmzZplCKV87do1jh8/zvTp0xnoOJBXf36VJLMkqlDlvo3xUSb5bDJrRq8h+Uwy1btWJ2p1FMlnk9GYazCzMaNqk6p37CM/J5/IJZGEfRMG6FNNXjtxDamVSJ1Ep9UZ3h+ZdoRzW8/Re3Jvg7nro4AS9IoHisZMQ+TSSBy8HKjVtxbONZzv66qo/oj6HJ5ymN3f76ZGzxo0ebHkCdCzUrLQ5mpxqn7/7KHPhZ5j18+72OC1gR/H/8jgp/SOWJ3pzNtn3+brZ75m/MTxnJt8jsoNK1N3eF2WL1/O559/Dujj+lStWpUGDRpAI/CW3uxct5M6devctzE+yvzm/xtSJxEmgsTTiThVd2Lk9pG41XEjLS6NpOgkLuy8QMbVDNKvppNxNUP//kq64XNeZuHIo5ObT77tOes+WddoAL+HmZLkjLUEtgMWBfUXSSk/E0I0BP4CLIF89KkE9xlp3w19ykEN+hSD39y/4SseNfpM7UPY/8LYNG4Tm8ZtwsnPCa8gL6xdrancqDJ1h9Yt1gOxJFRrVY2xF8eSEZ+Bay3XO9rm38yZ9XqrjapN77wKLCn7/tjHcvPlvPreqwYhn52djaWlJU6+TnT5uAtnvz9L8wbNObvpLBPHTKRul7rY2+tXixYWFvj5+XHx4kW82npxmctYXFPZrG5ww7zW0tGSrKQscq7nMLPzTPKzjThFCb0TlW0lW2wq2VCtVTVsKtlg5WKFNleLMBE4V3fGxNQEoREIE4GJxgRhIhAa/XuNhQbvIO9HRmVzg5Ks6HOADlLKdCGEGRAmhFgL/B/whZRyrRCiBzABCLm5oRBCA0wEOgOxwH4hxAopZWG/ZMVjQ0DPAAJ6BpByLoWotVFEr4kmZmOMIYn52U1n6TOlT6kE9K3YuNtg4176EAHntp4D9Oqg+8X2E9s5lX+KuQvmMnfBXOrVq0eDBg2YOXMmJiYmmJubM+mfSXh7e5MYlcgfAX/wfO3nC/Xxyy+/MHz4cPLy8vB39ydpXhJZH2Q9lOFwHzRjosYwp+ccTC1NqdKkil6IV7YxCPOb/1q7Wj+0sWjKmpLkjJXAjSSiZgUvWfC6oaRyAC4Zad4ciJZSxgAIIeYBfQEl6B9zHH0cafZKM5q90gzQ67JDPwtl+5fbMTEzoddfve5J2N8NN+zf/Tr53bc+mzg3wV3nzmehnxU6/sILLxjea3O1zOo6izMbzjCAAbSu2bpQ3YYNG7Jt2zZAb1Uyte1Ulj+7nKHLleWNs78zr516rbyH8dBTIh19wcr8IOAPTJRS7hVCvAmsF0J8j97DtrWRph7AxZs+xwItjNRDCDEKGAXg5XXnYFiKioUQgpAvQtBpdYT9LwyhEfT4rccDXYElxyRjammKmfXd5Y41RvzJO3vn6vJ1BmefHhN7UG9YvWLrVm5UGRs3G64cLV2oXcXjTYm+RVJKrZSyIeAJNBdC1AVeAcZKKasBY4EpRpoaW5IZNdyXUk6SUjaVUjZ1cyuaq1RR8RFC0OGrDrQe15qDfx1kTs855KTeXXq/0iKlJDsp2xBm4H5h42ZD4IDA29bJuPZfaAjbyra3zaW74e0NpF9Jvy/mqYrHh1Itl6SUKUAo0A14BlhSULQQvZrmVmKBajd99sS4ikehAPTCvvO3nen9T2/ObjnL5BaTSTiVUObnvRZ+DamTuNdzv6/9mlqZ3jZaYtqlNP6o+wcaCw1+nf1uuxF8fsd59k/cT8uxLanVr9Z9HaeiYnNHQS+EcBNCOBa8twI6AZHoBXa7gmodAGNxY/cDNYQQvkIIc2AosOI+jFtRwWn8QmNGbBpBZkImk5tPJnJZZJlmVrqRAKS0sXHuhHN1Z5Kik4otv3LkCnkZeQxZPIQRG0bg4OVQbN0DfxzA3Nb8jjlPFYpbKYmOvgowvUBPbwIskFKuEkKkAL8IIUyBbAr060KIqujNKHtIKfOFEK8B69GbV06VUp4oi4koKh4+7XwYdXAU8/vPZ37/+ThVd6LOkDoEDgykcsPK92SGeSvnd+iDmdXse+9B127GwduB6PXRxZZXaVIFEzMT5vaaS4+JPbB2s8bGzQZrV2usXa2ROkleVh7nQs8RPi+coI+CCsX1UShKQkmsbo4BRbwDpJRhQBFvFCnlJaDHTZ/XAGvubZiKxxUHLwdGho3k+JzjnFxwkp0TdhI2PgwzazOqNKmCRwsP/Dr54R3sfU8CMP5EPEKjt6O+X+Tn5HNp/yVDBExzm6JJqW0r2VJ/WH2OTDvCmtG3/5qYWprSZpzxdHYKxe1QQc0UjxSZCZlEr48mbl8cl/Zd4vLhy2hztJhamuLX2Y9Wb7fCO7j0Di1fmn+Jtas1b196+57Gp83TcnbzWcLnhRO5LJKc6zk4+joyOmL0bZOMaPP0iVIy4jPIjM8kM0H/EiYCM2szTK1McQlwKVXcHsXjhQpqpqgwWLtaU39YfUMc97zMPM5vP0/0umiOzznO9JDpeLTwoO37banZp2aJbPHTLqWhy9PddfIInVbH+e3nCZ8XTsTiCLISs7CwtyBwQCB1ntDHoteYaW7bh8ZMg21l21LH5lEoSoIS9IpHGjNrM/y7+ePfzZ+O4ztyZNoRdn23i/n95+Nay5XW77am3rB6t11NRyyNAMCrbcn9N6SUxO6JJXxuOCcXniT9SjpmNmbU7FOTukPrUr1r9VKlCVQoyhJ1JyoqDGZWZjR7pRlNXmzCyUUnCfsmjBXPr2DLR1to/npzmr7c1GjYgBuhD0qS/So1LpWjM45ydNpREk8norHQENAzgDpD6xDQM+C+OlspFPcLJegVFQ4TUxPqDq1LnSfqELMpht3f72bLh1vY8fUOGj3fiFZvtcLR29FQ/+qxqyD0FjDGyM/OJ3J5JEf+PULMxhikTuIV5EWb99pQe1BtLOxVkDHFw40S9IoKixCC6p2rU71zda4eu8ruH3Zz4I8DHPjzAMEfBxP8STBCCFIvpmLlbFVoA1dKyaUDlzjy7xHC54aTnZKNfTV72n7YlobPNMTZ//5Z5ygUZY0S9IrHgkr1K9Fvej/af9WeTe9tIvSzULJTsgn5PIT87HwqNdSnNky/ks6xWcc4Mu0I8SfiMbU0JXBAIA2ebYBvB9/7aruvUDwolKBXPFY4VHNgwOwBWLtZs+enPYZk4MJEMLf3XKLWRiG1Es+WnvT6uxd1htTB0tGynEetUNwbStArHjuEEHT7uRu2lW3Z8uEWAGJ3xWJbxZbW77Sm4bMN79rUUqF4GFGCXvFYIoQg6IMgTC1N2fDWBoauGEqN7jUe28QUioqN8oxVKBSKCsDtPGPV8kWhUCgqOErQKxQKRQVHCXqFQqGo4ChBr1AoFBUcJegVCoWigqMEvUKhUFRwlKBXKBSKCo4S9AqFQlHBeSgdpoQQ8cD58h5HGeMKJJT3IMoZdQ3UNQB1DeD+XANvKaWbsYKHUtA/DgghDhTnxfa4oK6BugagrgGU/TVQqhuFQqGo4ChBr1AoFBUcJejLj0nlPYCHAHUN1DUAdQ2gjK+B0tErFApFBUet6BUKhaKCowS9QqFQVHCUoC9DhBDzhRBHCl7nhBBHCo4Pu+n4ESGETgjR0Eh7ZyHERiFEVMFfpwc9h3uluGtQUFZfCLFbCHFCCHFcCFEkOasQ4nMhRNxNffR4oBO4D9yHa1Bh7wMhhI8QIuumsr+KaV9h74NSXIO7vg+Ujv4BIYT4Abgupfy/W47XA5ZLKf2MtJkAJEkpvxFCvA84SSnfezAjvv/cfA2EEKbAIWCElPKoEMIFSJFSam9p8zmQLqX8/sGP+P5zl9egIt8HPsAqKWXdO7T5nIp7H/hQsmtw1/eBWtE/AIQQAhgCzDVS/GQxxwH6AtML3k8H+t33wT0gjFyDLsAxKeVRACll4q0CrqJxD9egIt8Hjx33cA3u+j5Qgv7BEARclVJGGSl7guL/4ZWklJcBCv66l9H4HgS3XoMAQAoh1gshDgkhxt2m7WtCiGNCiKmPotriJu72GlTk+wDAVwhxWAixTQgRdJu2FfU+gJJdg7u+D0zvfqwKACHEJqCykaKPpJTLC94bXbULIVoAmVLK8DIcYplzl9fAFGgLNAMygc1Cn9x48y19/Al8CciCvz8Az93H4d8XyvgaPBLc5TW4DHhJKROFEE2AZUKIOlLK1Fv6qMj3QUmvwV2jBP09IqXsdLvyAj3sAKCJkeKh3P7x7aoQooqU8rIQogpw7e5HWnbc5TWIBbZJKRMK6qwBGgOFhJyU8upN/fwDrLpPw76vlOU1oALfB1LKHCCn4P1BIcQZ9E86B27pu8LeByW9BtzDfaBUN2VPJyBSShl780EhhAkwGJh3m7YrgGcK3j8DLL9N3YcZY9dgPVBfCGFdcPO3A07e2rDghr5Bf+BRffq562tABb4PhBBuQghNwXs/oAYQc2vDinwflPQacC/3gZRSvcrwBUwDXjZyPATYY+T4ZKBpwXsX9Ku7qIK/zuU9n/t8DYYDJ9B/aScUcw1mAseBYwU3epXynk85XIMKex8AAwvmfxS9BVLvx+0+KMU1uOv7QJlXKhQKRQVHqW4UCoWigqMEvUKhUFRwlKBXKBSKCo4S9AqFQlHBUYJeoVAoKjhK0CsUCkUFRwl6hUKhqOD8PwhyrOUo9ZtCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[650, 739, 746, 1009, 1102, 1419, 2127, 2376]\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "lakeTracts = [-99999]\n",
    "minTractPop = 5\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "\n",
    "        if y <38.8:  #only eliminate lake tracts south of Chesapeake Bay Bridge from map\n",
    "            plt.text(x, y,m, fontsize=9)\n",
    "            isSkippedTract[m] = 1\n",
    "            if lakeTracts == [-99999]:\n",
    "                lakeTracts = [m]\n",
    "            else:\n",
    "                lakeTracts.append(m)\n",
    "                \n",
    "plt.show()\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dca817fc-8a1a-4037-bb00-3f0e2e871491",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n",
      "working on tract 2200\n",
      "working on tract 2400\n",
      "working on tract 2600\n",
      "working on tract 2800\n",
      "working on tract 3000\n",
      "working on tract 3200\n",
      "working on tract 3400\n",
      "working on tract 3600\n",
      "working on tract 3800\n",
      "working on tract 4000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic MD map\n",
    "tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0 :\n",
    "        uncomment = \"if-redone\"\n",
    "        tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.4,38.65)\n",
    "pt2 = Point(-76.2,37.8)\n",
    "pt3 = Point(-74.95,37.9)\n",
    "pt4 = Point(-74.95,38.7)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  182 tracts w pop 216330.0  to reassign to tracts...\n",
      "[545, 546, 1417, 1418, 1421, 1523, 2069, 2070, 2425, 2429, 2499, 3507, 3508, 3526, 3527, 3528, 3690, 3691, 3692, 3693]\n",
      "I have flagged  138 precincts to reassign to precincts...\n",
      "[805, 808, 809, 810, 811, 812, 814, 816, 817, 818, 819]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x > -76.4 and tractGeom[t].centroid.y > 38.4:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x > -76.4 and vtdGeom[p].centroid.y > 38.4:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  216330.0 people (VAP of 172847.0 ) and  103003.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for western MD\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-80,40)\n",
    "pt2 = Point(-80,39)\n",
    "pt3 = Point(-78,39)\n",
    "pt4 = Point(-77.5,40)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  149 tracts w pop 201388.0  to reassign to tracts...\n",
      "[557, 558, 564, 565, 566, 568, 569, 570, 573, 575, 576, 585, 664, 668, 818, 825, 826, 1524, 1525, 2522, 2524, 2528, 2981, 2984]\n",
      "I have flagged  92 precincts to reassign to precincts...\n",
      "[649, 650, 652, 658, 664, 670, 672, 673, 675, 676, 681, 690]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  201388.0 people (VAP of 159890.0 ) and  89090.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southern central tip\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.2,37.8)\n",
    "pt2 = Point(-76.4,38.6)\n",
    "pt3 = Point(-77.4,38.62)\n",
    "pt4 = Point(-78, 37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  142 tracts w pop 263227.0  to reassign to tracts...\n",
      "[19, 20, 22, 23, 29, 30, 344, 345, 458, 648, 651, 652, 688, 689, 690, 709, 710, 712, 713, 715, 717, 718, 732, 764, 767, 885, 887, 941, 942, 952, 964, 965, 986, 990, 992, 993, 994, 996, 1000, 1446, 1447, 1448, 1495, 1496, 2093, 2156, 2777, 2779, 2872, 2873, 2874, 2895, 3144, 3246, 3287, 3288, 3438, 3441, 4029]\n",
      "I have flagged  75 precincts to reassign to precincts...\n",
      "[297, 300, 340, 347, 348, 349, 368, 433, 436, 481, 526, 546, 757, 759, 760, 762, 773, 775, 776, 777, 778, 783, 784, 786, 788, 1669, 1671, 1672, 1676, 1677, 1678, 1680, 1689, 1700]\n"
     ]
    },
    {
     "data": {
      "image/png": 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1FDz0EAgCYV9/xZIn36BHumOpHFuZxVEPN9zrCynatgSAsuxMagvyqX5pNoakJAJfeRmPqVPbPrfJgM/qvZT4KhnWaOQBHu75MFlHFnPcVkuYd1/gCBISpqoiqousuEga6hLUqKM8UEd5oO8bSO26HBq2FyMHgrpHkrczhSBNMII+gQ4D13DMEk+Iez0Gkz80qj8HHM6hX44rbpeNaTp3RUE95pTddB4WwpDerTdIDSYrcZ9sRTDZ0Xf0Ym6nSHpGNu8VRG9JYWKAF693CGHloSJmLdjPT3f2oVuYa5uutTfWHCY5r5x513dt4VqrM9axfuN6bJKNwcMGs/fAXmrqa+jdt3crt9upbrwyexlWewMSEvWWeqyiFWNDCXa1Cgsi5Zos5DIrP6elYbFbsEtnkIj6wy4D7Frb9mGtQsu6q9bhrnZvf4yLmHMy9JIk2YFujf723wRB6ATcCTwkSdIvgiBcDXwJjGpj/B7AfY0Ph/eBJ4HnTj+HIAh3No5JWFjYX7wcJxcCc5PnYrAZeKzXY//aOQ0HDpB3+x2gUBAyby6uI0a0apN7++1IZjPed92Jvl8/ehQfazrmba3h5Avqzx9/0/T5Nw/cwYgjOQS/9GK7Rh5g08PXE1pno/quq1sdC9L6squ+hh8XLXDMw+TC6+9/ypVCP7zQ0+BtbqprKndV4XllLIaUUiSTiPl4Oj6aYPIbjlPW+xt0QLzqGKWiJ9tiNhOXl4iv2Rd9QwM1v/3WwtAf312MIAj0HBve5pwfmbsbeZkjB4w8o5aoMS1XtNFaNWvLa3ghOoie4Y4gsqOF9fSL8kVD643ZotIiQvW+9Aro1fSZwWBg4cKF+JX5cfPNNxMWFoZPng+7M3dzZ+c7kZ+hYld29jwyMt9m8KB3UKkcDyDzq14YA6ORTVxNl5c38URsAXdfdycAdtHean/n5JvLa6sOc6ignLmnPYQsooWM6gw+TfmUIxVHGBDU1nr14udPqW4kSaoWBGETMBa4CXig8dAiHBuup5MP5EuSlNT4+2Ichr6tsT8DPgPo1avXfzvV3EVMelU6i44v4pq4a4j2+PdyutQuX47Y0EDMhvUog4NbHa/84QdMyQdRRUTg99BDAMQdSuH44CHsc1dT7NH2Jp1VLqeoezCdrm5twE9yZM2PhP6RSn6cF6Nue7HFMVEUqanNxywXyHFbTrnNE70xGk+LF15Gx8ZmwFrYnPQT6gF+9Bo4BNuuA0gmh4vEdOQIqojBRIk/k10TRW6IgXiOYbZVU2I3Uxq4hbfstxI9eTJV8+djyc5GFREBQNbBcoI7eKB3bzty9WBpHXE6Dd0HBvPT+gxGfLaDrXcNxKWx8Padob7cm5pLttGMvc7hcW2v9uqJ0nqOFtUya1jz3zwzM5OFCxdiNpuZPHly0wIuNDSUnTt3kpGRQYcOjjeNQ4cO8csvv+Dq6opKpcLf358RI7oDUFt7EB+f4QDI7RKSXEFJfiYAwb7eTeeTy+TIZfI2H0KdfUXWHzxOJ+/uaJQtHy415ho+TfmU1IrU/66hFwTBF7A2GnktjlX7Gzh88kOBTcAIIP30vpIkFQuCkCcIQpwkSWnASODo3zh/JxcQkiTx5p43cVG6MKvrrH/13Ia9+9APGNCmkbeWllLyyqsglxP23bdNn0vlBfiM6ULMsl0tDH3vLv50u+Ye0n/7mtrMQILcM9o9b+rmJcgemA1AwnOvttDCr8law9PbnsaisAACm8RaenvaCA9xIdwzgeJIDaHKQNJ3H8TroBteq1TkrNmIRtQiYERCS11cKN5msLg+xz7pRZLyKgnTqhCFRO5Xx/CBeSWHbCkMv+V1an75haIXXiT8228wNVipKjYQ1y+gzXnX1ZkpwU4fH09eHxWPwWZn2aZsLvtqF9tnDgLApXG1bZMkihqzP4Z6tq2l+CEpF7lM4OYBEQBYLBaWLl2KXq/n5ptvJiCgeR6xsbEIZhM//PADAF5eXlRWVgLg4eFBXV0dR48eJSHBYZ6Exj0eU8luNHYRhaEGs8kxn7YCrtoiolF5k1NhIC6g5ZuLu9qdYJdgUitTz2msi5FzWdEHAt82+ullwEJJkn4XBKEaeF8QBAUOT+GdAIIgBAFfSJI0vrH/fcCCRsVNJnDL33wNTi4QtuRvYVfRLp7s8yQeGo9/7by2igrMx4/jNmFCm8dzb7wJ7Hb8n38OpZ9jR1Aym8i5eiLmMjtuwC2DOmHo3AOv+D7oAiIA6BCSSU2FPzCYmjVJuF/Wt8W4j7w+gtu/cQRLFU4fwsheQ5uOSZLEczuewypauTxkOFe5RNOhy424aD1bzc8/LITC3rvI+vEDomuHYRd8sU9RsXNdGgPrujW1S1cJxIgC46wRjB0/h+MnjqBLXsk3mr2ML03F58YbKZ87F3t9PaU5jnzt/hFtK1sOpJZjF6BzqMMn/cHYjqRXGkhNKSWzsoEoLz3yxmdWnU3EVeMwtsW1JjrT2o+9K7OCHqHu2A01pGQWs3PnTmpqarjllltaGHlRFFEqlbiKFmobV95arZbExEQmTZqERqPBYrHw5ptvkpl5EBdXUCm9MR5biGzJPdjlAsLIl7DXOd545LJzC4CKPKVQ+OmGHiDRO5HUiv+woZckKQXo3sbn24CebXxeCIw/5fdkoNfp7ZxcWljtVubsnUOEWwRXx7Xv5vgnMOx2bKjq+/Vtdaz03XexZGc7ol+vvbbp88pX7sFcZifwrom4XDMLRVAkp4YsSWYjdYe0yGQ12I0itetdaNgxH98HrkDp5TCeHlmO/DTmL15l5KApLc57sOwgRpuRoSFDeXXkB+3O3Wqu5dDS20lMXYsXkBKeScTEj9l94jdeDV7Ks5obCXBPxDcgkCXd17fo27N3FIv9Ihm/aQbbdy1iqq9j41tsMFCS3QAC+Ia3bej3ZzhW0L06NEf8XtM1mBdTSvk5tZinBkYTqnVsZGebzFwV7olMgJT8akYn+rcar6okn2JJzrx5jt1OV1dXpk6dSni4Y39AtNtZ+P4c8vclgSQh2G0ogiKxu3pwxx13tBhLpVLRvXt3cnJ+IS4eBJkC076P8DRZqJv0P1xjp2HfswMA2Tka+ohTDH1bJHglsC5nHXWWuktSffPfjAd28rdzUk75WO/H/nXNfMOuJGR6PZrT8sWb0tOp+OxzBLWa0C+bt5Dqvnmd0kXbcenghvsDb6AIimw1piXlIKLohsdAgYDHBiDIqxDNEZS91yzbuPXR7xAFyP/yY8oKW7p3XJUOY6GQtVxLiaJIeUktNqud3Iy1VLzdgR5H13DEJ5zqOzbQ565d+AV3p7i+AFEQGTvqcgaPG0d8925tXntIWEfUNoHcmmwElcMXbzebSUsqxi/MFbW27bXckaJaVBJ0jG5+w7gq1g/kAuvSHPnmg9QOQ59WbyKnogFRot3o2AidlUpJx+CRl3HHHXfwwAMP0Lmx5q6xoYF3brqagqRtCDYrrqGReMZ1Qt8YGdtWAZGePXs2VYWSJDtoPJAAXefbGu+jQ2HTXgTu6bhplPi4qNotK5jgneC41sq0cxrvYsOZAsHJ/5tT5ZRDQob86+c3JCWh69UL4ZQMk6IoknvLrSBJBL/zNnK9HvP6b6n6+Seqtmaj9lEQ9OVvCI0rwp07NqNVq+nW05E3xZKWCwSi7t4FeZAPIa9PJff+75EsbogWiyMHTmw3ksd1JnrlIUpHTOTAdUMZ89wnAE3+3hDX5ujPdVt2sm9ZHq71jlW0uzKPQV5e7B9/Jz37PtjimuaVO95SbGeSDOLIzuhmEqhQmBEaDXP6vjJqSo2Mv7tzu/1OVBsIlitQnLIx6aJS4B2gJyO9kneTtzIgyLFy31ZRS8muYpRygbEd2/b5e/v6Qb2dbl274O2mRxRFvnjucWqOH0VSKBFsVgAmP/cq0Z0c+eQXfv4JNQXFlBQWEBDcMko2ICCAuA7xwBZqa6pwrc7HLhdQKBy+dkXj39pqO/P9OZUIbz1ZFW0b+ngvRyRVamVqC9XQpYJzRe/k/835kFOepPKHHxyumZ4OL2LD7j2kdupMWmJH7OXluIwcgevIkdjWvcv4xSauk09GE6gk6I03kPs6sh4eSNrMjGX1TF5UwdPvfsq2HdsRq6oBG7LAZgOk7eiHoPKk5M2lTZ+Nm/MjtnefIbOHP6ELNpO0yJH6YHnmcgAGBzXHESStyEBp0SD1L0amMlBjjaR64HME+I3lyOov2LXkY45uWkRteTFT/B1uqC83P33G67dYTJS5iAQqfdB06IAkk/HD3jpqtDIiurROxAYgihJ5Fgth+par88q6KqguR5Dg7a02rjhiAKON3I15rD5SzIOjOuDr2raCJ7SxktPGlCyOHzzARw/cRc1xh+5CsFmRgIBe/ZuMPEBMYicAjuxtlTkFAF8/R3I4c3UBrvkZVEVEQ+Nmt7eXQ21TUVPXZt+2OFOhcB+tD35av0vWT+809E7+X5yUU14dd/W/KqcEMB45Qsns/wHgNm4stspK8u64A2w2NF274Dp2LMHvvw+AfOgsMjxCyPAIYWK/F/k1u4SaegMAGw5mNo35Q0kINy6rZHGhCZmsoYWKxufO0WDPxVbriym9AACZXE7ncdcz5uuVFPvIkb06l6Wv3snu/J24qdzoG+Qw2KIoIrMokftbuPema+mo2IhKaECU7AR804+Oux6hX/KTJG66HeGj7jw2YA59BB37a060e/2px7bzzKfTADhiy6Hcw4sHHnyer0e589EEd25acZi0kvpW/VYeLMQGGJXN6QPqjHXc8sWvVBuU9Ait5X8D4Sb3fHTHyrDWWPHRSNw6oP34llnjeqHEznfrk1n2+gtYSx2b1JJcgUtYJLO++JHrHnumRZ/EHj1BtJOaeqytIfEt2M/ILeVELboNQQJlt9ubjnl5O942KuoM7c7pdCJ99JTWmWkwt502IcE74ZJV3jhdN07+MifllHql/l+XU5Z9NJfyefOQubkR/s3XKIKCyBg9Bslsxuf++/GddXeL9oJKTYy9hhNyd0QJnj7kz9OHNhIgq6FY9CNIVsXq569Bbq5l1oeL+V99NIVYWkT2CYKA9y39Kf8qk/IvthH08pXIGrNGqtU6Iud+QuZTjxLzyxZu7yhgf/AaAOx2kbffWYC7MRjXGMeKUqbSIhplKLfPIZ1g7JM/w93VlexlrzGwdiUVNSXE6IP5rTYNu7kBubo5mVjR8QO8t/I5Vrlno1EL9K/2ZWLEFUzcdpDyiEgeLCvjoNqP9S421h1OJzoJ4hRKwpRK9CoDoiENZApKbTmsT9FyJO8Ev6ZI5Nb48fQoO3eMmgFAbm4yURGP8H3tteSZAhgwewUTEzy5dVQXIgNaZtt01WtJ1Bs5VqtjkFzJ8DtmEtulOx5tpHc+iUarJchVT2G9kez040TEtoze1bk0B3pVj74X904zm343NDhW8hrVue8Hnawfm1XeQKfg1sqhBO8EthZsxWgzolVoz3nciwGnoXfylzkpp3yi9xP/qpyy7o+NlH/0EW4TJ+L/zNMoPD3Jf/AhbIWF6AcMaGXkT2K02IlRVLPq5SvYtmkNR/JKSauCDVUa+nvW4aZRgsabLx69gdgXN3Jc1XrjUZsYhdw1CdEYwomXP8DeIQOzNReTVIpFXQv3WCnWQrwV8qoX89P2HVgPXo8+IxgxvpLrp10JgC66I7Z9WuIo4VtxOHd0d6RNKO89AzaspCg3nT6B/fihPp1l214lTNMTwWpj8+Hf+UG1H6ubwBWGOO6f9BoeQTE8/uMSioK9mKc0c+UYR473tMJaPkjJZx8m1iqtWOUWPApfRGnJwr2DgvzCq7j9hyrAG3d1A+9OUTC57+VN15qe/hMxHtksvX8QKw4YeHdjDvMPNzD/8E60go27+vjw4JTmdA8RGhsHG1y48Y0PCQg+czGQk0y6ZjqffP4lSxYv4sGnWq74PQbdR92mt6nVRRA88JUWx3JzHW9h4QHtP0hO56TEMruiHUPvlYAoiRyvOk5X367nPO7FgNPQO/lLnMxOGeEWwTXx1/yr5zbs2QNA0KuvNOWyqd+4EUGnI/CDDx1uktNkd0UZeRRovbjatZZ3FizlqxN6bg6V8eETd7Uaf8PKbAB6BHq3Ogbg/8xVHJr/KBVhK0CQwAbKOi1qmw9upgCsNneS7TsIdCtCay7AEJaGiz4WS/m9pO8pwTfMDaXeEaBVL3qT6Nfs99Z7OlwSpvIstJ7eRDbYeD5/GbDM0cAV+tcF8MToN4mO7MH2vck8kbSeE8FRzCjM4srrmmWecUFuzA1KBMAuSdy8+UOSLVn09buOouLV5Ab/RPfIdTw7/Dti/UNbuKmsVhNG03rk8kC8vOK4YSTcMLI7D366giVZYJQUvJdUzZrUJXx482BigrwpqbOglSznbOQBAsIiCPV0I6+2gdrKCty8mu+5TK6g3G8gISXrEe02ZPJmc5VbWAzoCQs993QpJ4Om2vPTJ3o77lVqRarT0DtxAvBT2k9k12Yzd+Tcf11OaS0uQubiAo1uk4bCMiSzI0DoyODR2DWudFrzK2q35ijOJVsdfuCFdW4IdSISMj7NC+HW3BP4h8W0GP+T3bn4IjAu28jyr5IZNb0j2sbyeHazkf0bbqM2PAkv42hi+zyIzjsG2Wkyyt6N3zcd+xBV3jx0ofvISU9l/dcn/cOO1WWJJQaVvdnwlG79nBig1wGH0+ju1AC2eYr0HX8rCr0eX5cAevedDMBj3//K94ER+MoVvF9XwrQZV7R7z7p/1xUJCRfX7nx22ePYxId5esMM1hQfZ2XmezwY8E6L9kuXjcTTswqdrqXG/b27JvBoWTWD3t4OQGqtkis+2sbNPbw5ZNTjL1a2O4f2cPH1p6CqnOKS0haGHkDwi0dZspqawuO4hyRgTdmJ/LcpZFknIDCFkNCocz6PTqXA301NVnnbfn1/nT+eas9L0k/vNPRO/jRVpio+PvgxA4MGMjj438tOCWAtKaFu1Wo8pl+DIAgYy2tY8czvnBQSqo2VYKwks09PZCrw6BOK4OpFTZoLdJlCV3kR70yLwySomfRjMY/M38xXj0eiapQZ/n6wkGTsjHDVUSYp6H68jtzZO6kaFkSXXhqSk27FqM0gjPuJHnffWQN2hsXfR6F6HKlZlxHWrRINodRVmDBW1VKUbUYmiFgbqvlh6wrWFam4rKCMAQrY22EGbt2mIb35I1fsOUifTx5pGlMURX5du4n5wVEMKi/iyzGDcHc/c5CPt0sk5fWZjI6chEwmQyVTMWfMIgp+6cPCzA3M7GNE0+iXdujaHav7jok3tRorxNeD7NcdUcjvL9nOZ7vLmLu3BuR6YuTl5KSnEx4be8b5LPh9KyuT8yk3SeRa9Bhl3hz64SiLHwgixKfZraLwdfjt3b/sjyhpUQqO1AdVuKLFhkz6c3qSCG892e1ILAVBcGzIXoLKG6fqxsmfZm7yXAxWA4/1fuwfq3PaHnUbNgCg6+nQOq98bjnlyhDy4yfR4BuL/uX30XdTIteIKDzUVG7LpWLVQa6T7yd1WDlLX7md6G6D6di1D4/ElrKtLoDklANN47+03CEJfOnuPgx6ZgDlE8OploFmRxrJu+7ApMqjg+trxI544JyjMgMjorGb3ZBpMxl0VSzj7urM0GsjAFhV2JniEitPr4CN+y085TWLgkeK6HXtJ3RIHEnkru24NjQrZ2pqa5nw4wruVTs2Q18Y1OusRh7g3eFzkQQ1v6W8RI+FU7hy3YuAwA0J11JnF1mY8nZTW0EQiIpyZITcs+fNM477wOSBHHp5Et9Mj+OWSCOdFcV89923lBTkt9vnu1838My2WvbVqqm3SnRytTE9Tk2ZVcmsr7cBUFdSzepvlvLjH8knS6tjde2HTekIbuvZ5wYMqNi1ta0SYe0T6aNvNzoWHH769Op0LPZLq2yGc0Xv5E9xvrJTAkgWC1Xzv0cVHo7bxAkc+3ETxVIQXUMrGPTZGwDULHkd9/gccqe8Sdg1d2E5lgz1Zah6jmrSYJ+k3uLIlxLeWJjkh6QcyurNDIrxIdTL4VrpNiiMpHoTFSpHiiaNNAp1TG9SczZRXV9CVPAw/D1apwRoMW9JQlAYkduao1Dd/PyANJBpyGvQs/B+f/4oz+I9YwxzT2zk1e4TWo8hCNz/+0YOBIXzWHkek7olEhvoe073rptXCPPGLuKr1N/Ym/M96YUn+C33cq6Iv48PUr7jh+O/cX23p5seXt26Xs/qNT+jVP6OyTQbjab9h4lMJmNYtxiGdYshJSmYX1eu5rcfvmfmY60T1dYbzLy1qxJfmcSmlyah1zZveJs+XcmSLIlX3/kRqfYEVuyEq6I40PF3ug3viNqtWekzwmBBsXMd6w8XM2TMuf87jPDRU9lgocZoxV3b2uWY4J2ATbRxovpEk8/+UsC5ondyzkiSxJw9c86LnBKg6KWXsGRl4TJ8OIIgcHRLLiprHf0em9TURnfkMwA8OzoCc1Tx3VD1Gt3KyFeWFfNZjj/DXfPx93MY6l8POLTx713TrUXbPpfFIloceniTsJ5DB0ZRmHEbhpKn2bpjIruObT3jvHNPbEcmt6LTNfuTlRolYEMQNChUEr1jenJnj/FEyQr5qjqYj4853lzSH3ocgGMHUkhJOcKawHBuykvnkWmXExv75x60Q/wi+Wbow3x/+e+O6z2xHLlcyTUxYykwW1h3/KumtjKZjKDAqchkIimHfjznc+j1ekBCpW47sOqHlduplel4dGhQCyMPMPu6Ybhg4ZcyOb4KT+685hZuefpuelwxGJlbSzmnm05FTxctm8vqkMRzz2repLxpb0PWq3lD9lLCaeidnDNb8rews2gns7rO+lfllACiyUTNUofyxPPaGZgqqimx+RLi2YBC02xUjFcuAqBu289nHM9qrMOGgoERzStVs9Wxwvd2UbVoKwgCo8f+QLdee7C5vkSp7CHq9O9gc38DQRCozr+ddRvex2qqadHPZjaS9se7pKU9gM3oRkLXluokSTKBoCFLE0ropn0k7kgnU3KkWf6johaA/pPGYVEoKHj8cT5bsR6Z3c79Y4byZzlSfoTMGock8emkDwGI8nTkd5nR9Vn0Mlh+7HsaDsyldvdrVP/xAGFSDVqDHVP56xTkp5zTeTasWw8IjJ86rc3jm48WoBYtTB3dv9UxNxcdM8O8qZD0uEcOISih7YIpJxkR60uOZOfE0bJzmhu0lFi2RYhrCK5K10tuQ9bpunFyTpxPOSVA5TffgM1G2HffogoL49CnKxDlWjoMa7np55bQk3oCECpalUdogX9oDEGy3ezMquF2oKDKwJHCGrRKebv7Dt5uXlzW+/oWn5XXDGbLpttwd/2ALdvm4mUajp//WGqrjlBhXYtZW4CEN4U7r6bYv4yYXg73jd0uYpdpEAWRkphgpupS8VXrCdO5EuvmTzefsQD4BgaQ8sTTeL/5Oms692RodjrBo1oljT0jFruF6SumAzAkdDx5JStxd+vF7B6Ov6NG5UInUWLqiWPok1umXDhZhkNMGkydzB2lZEYjmSifvhKf+IEt2lptdo5XmWiQXHjqu63EBHjQNyGUfl2iaag38P4v29lV70YnvQmFou3KUnfPHMyS2Wt551gao7PVeEXEtXtdYwaE8tqBXNbuySe2k1+77U4lzEuHILSfxVIQBOK94y+5Fb3T0Ds5J86nnNJeW0vl9wvQD+iPvk8fADKSy1DafIgY23p1W+M1nODKHymYcy1uVz6Ha3RC60EFgcvDzHyWHUplYSbT52cjSjD7io6t254BH3d//F3fw3f3JmrC91Hls5HKuvWgAI0tgoad95GX53Ajrfkin43fJ9N1ZChZ6FBIClKH9eHra7ugULiwLzuHtSey6B3n2SIyc/C0K5mVVUCd3oVZXVrXfj0bKrmKcLdwcmpz2FiwAzlQU99c/6feUk8SAine3qzq0g9VxCgU+hDshbsRjeUYqouwVNiQKgoIr9/vuO6fxiM+V4nslFKAa/ek4iKzkGINJLNax8Yakc/TcmBJTmMLOR3kFbwyvf3Ed2arEVePQjJLvPnj8PUknphAdJ/7ULu1jmmIDPUgUqFgU04l95zjvdAo5YR4aknJr2m3TYJXAj+n/YxNtLXKPnqxcmlchZN/lPMpp5QkicKnn8ZeXo7vJ47MkNbaeoqtvoS61SFvY2Xoe/MbFHwnJ6DsR4TvVpEfeAfaQddjKsxGLDqKXDKi7TaWfomRfJpt4MGFh8irUpAY6Mq0XqF/eo7u7np01XFUxQ9m4IAXqc5NwSOsB0q1Jyc2bSMPG6HxDYh2iYJ0N/asMAJGbDJY76nmlQ1JNCBjgdwVu9yDD9NK6XL0CAtHDMVNqeWVtBx+HzyKqWmHGDzzhr90H5dPXs6z6xewtHI7mLaBaGDW17eilqtZLzmULkaZDO8xXzZ3avxbn57RvuitQQTWH6L01U74PnkQuVKF3W5n38pd6NGx6KlrcNVpSM/MY1vyCY4V1WCzi0wd3JFBPdsuDgNwvDCbO7/dSnaNHzN7ZhEhxFMkm0/JrkW4Wfvi4zYMn7Dh6EKCEeQOr/OwIA++yy2nusqIh+e5pS2Y2CWITzdnkF9lIKSNilkJ3gmY7WayarKI9TyzTPRiwWnonZyVk3LKR3s9+q/KKUWLhYKHHqZ+wwZchg9H28mx2k7/bSd2hZaYAR5t9lO5uRN871wa8h7H+O2NhBR/Cos/bdko+z0C3K9BwQS2FDv+Gyy7d9BfmmfZ6kwUSHh38kGt98Y/wZGCQBRF5JgY7FdHlwcdhcWN9UYOrk/GVFfPao0Fs0rHxyrHajW+PJ/H46OYnXKU5KAY+mzeyAvxCXxWaUAjE3jr9mvbnUN7SJLEsRMVfHSskCW6jvipDESLFqJEFzZbdmKQjNAomx8s6M84lrEkj+qFc9AMmUvWxnuJNKZgfKUTGQHvYysRuNUahxEb+7YWMGJcLHEx4cTFnNnPDnAiM5vPty9nWVoQMkHDO5Mlrux3L3AvlZn7yE37gmrFLqqtmzlxYjaa5AgiQu4muOdUJvQJ5evccr749QiP3nZu6YWv6xvGp5szWJCUyxNj41sdb9qQrUx1Gnon/w2aslN2uJoYz5izd/gbqf9jI/UbNuAzaxY+9zSrfDL2FqGwexM98cyGWR8ajuyuRRSs+RqFqwdqvxC00V2wSwpq5z9IQs3PfKjM5G7rQ/gIdSjkf16bUF5cT3SdncNhesbHtHQvyGQybIIVxObPtC5a+k12bEQOA16TJNLy8zEj0GlIZyRR5PArj7F2UE+Oxo/ioeNVIMiItJuxmY2gaLuIeVusSi7gw/wyDugkFDq4VlTz1PAb8PJwaOQr6sv5bsVnfGVyqGoUtO03NxTlUbPjd5THFhJo3Y+0YgGuMkeOGS0leGbLkNBRGSnDnCsnenMRBz00dO1/9rejkqI6Rn12BIiiX0gxs6cMIjaoA2ZjBWqtN15RPfGK6oko2qgu2kdZ7h+UWn4nrfIZvCr60atXMCPWpPFFegnXFNQSGtx2Ra1TCfHUMTrRn59253L/iFi0qpbXHe4WjlahJbUilUnRk9oZ5eLCqbpx0i4t5JTd/n05Zf3GP5C7u+Nz90yEk4WqTWYKTL4EaqtRaFRnGQG0vn4EX/8E/lfchUf/Caj9QtH5B+L/8I+Uj19Ox6AefC/7kJn6nLOO1Rap2/ORIRA2oHVR8vxtKegFN+QB7bsUBEEgPjSUrqEhyOVycgrSkEsC0z2VrOkRRkjBYcd5BBUdtx1hxJK1/G/9ZoyW1gE9VQYD9fWOTcYfdmZxS1UZpXKJh5Qu7O2bwJzRiXh5NM/FXePBuoaNRNpCiBQFFELb5qDsgxsIPPQknpaDFApXUm+fgNUWTp3tSgpN85HQUeelwcXPnw6P9qJBEGHFUUTr2YuCSI2xDONR8rrOA/nCfWS/uJyyl46S+cWCpkpSMpkCr+C+xPV/iq5dv0SS2cjc/TEAz13TBRF4eUHyWc93kjsGR1FlsPLhH6037eUyOXGecRytONpGz4sT54reSbtsLdjKzqKdPNH7CTw1rYta/5NIFgt1GzfhOmIEgrJ583fzJ7uxy9UEJZ6byqI9BJkMnz5DkHoPZuE9f2BRta37Phu1xfWISCR0bBm4ZKkzUL88F7kkJ+aGc5dDnsg6BEBIWAc6eYYz4/eFWBRLCH/oGVYVVpOMgrlyd5at3MIkNzWDwoLZXFjC+moDGXpPFHYbStGOQa2lW5WNT0O0hPdp2/2wfPti8uTFvBr+PJ9mzm7T0EuShFpowCzqqJm8gqAePchemYVxZyFKu4jRX4ZniYhrpQmSiqhLKsINOdjUHPv9WxKn3HrG6006VAyABwKK435IugoIMWG1FqE6EUbOTwsJm3YlcmXz38ctKB73Q/0plS2jQ8MTRMZ4c3OYD5/llrN9Vx4D+539TaJXhBdTe4Tw2ZZMJncPpoN/y4CwBO8Elp5YiiiJyNp5AF5MXPxX4OQfwWq3MmfPnPMmpzQcSEasrcVlWEsj6R/rcI8U/Ant9JkQBAGLqwKq/lrIu0upkXw5yJUtX/9T56xEjxuKoZ6o3c/s+z6VohxHoZGoqE588bkjsZluSBTX9urJ/EmXcWjySJ6WG2iQyZkruDIjr5ZP7FrqbXauqCtipLmaPvXl3FhWzmv7qjEtyqYys3U6ArvdzpcZXxNhC2b84CsR7FbEmrxW7QRBQKbWUa+KwTMujqrUo4hCBrsqS1hTZWVLmpmNtVaOGO3U2K3I3TNw71aART0bl8PvIolnXtVvOu74O6pDXfF6PIKwpyYQecfVhM+8CktsHsqUEHLfWImporRFPx/3EYjKBkzVhQA8cEM3/AUZzy4/SnW18Zzu9dPj43HRKHj2t8Ot6tYmeCVgsBnIrf1zKRYuVJwreidtcj7llAANu3aCXI6ub98Wn3eakEjBpgNk1PhRnZ6PR2xIOyP8CVQy3GvspJ6oJCHG6+ztG9mxJI1oM6QleLT4vCazCG+LPzWBNXScMPGcxiqrLmLN5p/I25aEoBWJDOzA4q37MYeoee7211u0vX/IAO6TJA5kZbM9J4/+Pp70HDqqqf7tSQp8DyKtqaXs02Q8Xw9usZG+eudScuSFzA59Grlcjl4SqZbLqVwwFJlnBJLdjKbbTLShwxz5zUQ7q2f/RG5NJOBIihbhlYWru0CnKcPwjIlocf4jS6rol/wkhzf/QqfhV7d73TUGK6FyOS/c06/F5zKZjIhbppP720KUe4KpzUxD433KW5zkuJa8+cewqxvQBOp4KjGYR47kcdO72/j4zr4EncVf7+2i5smx8Tz56yF+3V/A1J7N/5aaUhZXphLhHnHGcS4GnIbeSStOyikHBA341+WUJ7FXViH38EDh2dplFN4zmBObRLLWHaT7/9PQ19SZ0ZVZsAgg2c89lB4gbJdjldlrYstN6iO/riGMaHJ0eax//0Guv/5JXHRu1NVXI8hleHs1F9g+nJbE799+iDyzCpkkIFdLdL/1ehSCAoVNQOXnh0Le+r+pIAj0iIqkR1Rkm3M7/N5y3IpckQlyLG62FkZeFEU+P/4lwZI/lw92RLBGakR+l7RMNZXxSfIR4ixWrAfXUNVpOD7iIZAkyuocf4ux442EDxuEws2j3XsTNXAqJD9JQ8GRM95DvVyGWWr7vstkMiTRkdbZNapluofCE1YIBoVQgmuxJ0JJA72A59HyitnIZR9u5d3RiYwa2fb9OcnVvUL5eW8er65MZVSCP+6N6aijPKJQypSkVqQyLnLcGce4GHAaeietmJc8z5Gdste/n53yJGJdHXKXthUmWbsLkNu96DC5b5vHz5V7X1mCW5mNYLyImx5NYlzbhUba4tRX/ao5eznQw4dhVztWgaZyR/qC7E2bsdnr+WZHc61Ti0Lk2rc/QKaQ8/VnL6BNqUBSSNi7B9F3yCQG9BqHQqlElEQkwGwz/enryt26H49iD6o05XiNjqbjgJYRrOuTVpIhz+XZoEdRKB0m4L4rVlK8aSZ7q8v4umsvnku8F2nJXXgmO3LuWNDgGb+KyNjNGGX+lJ4YhkbmidI9BtfI1sqU3H1r8QNcAs6cj8fLTU11TT1Wkw2lpg1zpHRs1gq03HhPGDOJffvnktHhJwb1/w1rpY2yvSX0zKzhU5uCJ631PLIulSUdvIkMbX9lL5MJvDy5E5d/uI05a4/x8mRHwmulTEkHzw4crbw0NmSdht5JC05UnWDR8UVM6zDtX5dTnoq9vg6Za9sZEzU6OfYGJYaSavRBPhzdf4JDyakEBPkzeHQvZGeRSYp2kZ++Wo6PNRk8IN/ihbfVFYstBFU7ofmnIwgCns/2IWVnAYoNeXgeKEea5sgwmRVSR4d80MhdqFLWoe3r2Aw11dSiTi7luzeeQKgwojMLCF2CuP62Jwk9zSBuSlqGQhQIimgjqvcsVO/LxQtvYu8bhcan5T0URZEvUr/AX/JhyrDpTZ/7ucayr8HxUN+nmogYPALXhwqwGUqxm8pRyPV03vcgRpMHVVIBJdU/ODpWgtfRZwgJvh7fbk80jefqE4AoCXhsf5njbn506DO2zbn6e2iw5EFRQS1h0a3dZu6JnWjYVUfh1+vxntYZ93BHLIWnvz/BAc9TWPUYe/e+xvCJr+OV2Pygfnt/Ebf+vJ/p83aw9JEhBPi0Dow6Sccgd24aEME3O7K5qmco3UI9AMeG7NrstU2ZQy9mnIbeSROSJDFn7xx0St15kVOeilhX76gi1QY9bx1Cyjvb+GbhIRSrNdSYSwBIzYUdSVvoFN+NhgYDlZXlGEwNTL1mMpGxDiWG3Wbn43e+pdyQi5c2BIu/D9bsQxzbtoLnd27i8ftm4uVx9vzuAHoXNf1HR7EipZSuZVa2/J7O4PExdOwyAvJt6Af25Ok772rhu373nXvRJWVjVQj0fXAmg/tf3ubYRw/vAmDEGfzbbWGpM6AuVFIvqyHEp/V17EzeTKo8g8f87kV1ipJFIROQyfRYFH4clvdnws5NLOzdnwCdHwqdwzceM+I3ACSLgYbs5VjstdQWbaBAvpeUys9I3FlKYH9HXvu4XqNIs/+EbvWDRK64lqSC2fSdcm+LuVitdn45WkKATEZASNurbq8OPTCPXYO43ovaT0qp6niYkKmTUWjVJHS/kqIVG7BrF3P84GV06Dq8qd+gHoHMqUnk4dVHufrdbSx9fAie7q1rAJ/k4dEdWJFSxLNLDrH0nkHIZQIJXgksPr6YwoZCgl1ay2cvJpyG3kkTWwu2sqNwB4/3fvxfl1Oejlhfj8qnbVeKW1QQLm555KnqUJoseKiDGDJsMOUl5ew5sIt9qY6QfrmowS5YWLlkPfc8dguiKPLV3J8oN+QSF9yNa25zVFsymcfzxW/rKT+2mzc+nc//HrnrnFf2AF2nJ5D3aQrR20vI2F5MSGN1ppI6Q6sN0oce/og6Qw1qhQbVmSSdZodv2t3j3IpfW4xGMhdtx3K4Gi+ZP/ahrccWJZGZh+4HoF+fSRyrKWZF3j7qrQ308Y3F3z2BwvItvBdWxaN5PozbvY+fu8fTwb3lPoig0uHSwaHE8kq4gzBrA0nru1NkXEvgKe3i+o6lJm47xz++kq77f+FwnRudbryx6fiKdRlk2G28PTAalbp9UxQ47DLMXcsp+HUNysMh5Jb+Sti9kzlem8NdumvwlYYRW7aT247EMSg+oOmN7orhkZRVm3g5KZPPlx7j8Ru7tXsOV42S5yYmct+PB1iQlMON/SNI8HK8TaVWpDoNvZNLA6vYLKecHj/97B3+Yez19chc2l9Zu2saKLTaeeCem3AJaN7cHDauH5lpeQSG+uHu5cLXH/1MTnkqqxZvRhIFCqrSCfOOZ/rtVzS9jmvUKu6dPp4PfxSpSNvL029/hourG6b6GkSLEcFuIbHXQG6a0HYyrpBgd7we68OW7w4hmu2oAvX8XrOJjZrvyFmt4+2xd7do76pzb3OcU6mvrkRSirjozh7pCXB4znL8DIGYBFfqY0zEj2u9ib72yKGmn6f+PqbFscWN3yVBxTUxo/BRbeeuDA2X78/k24Qa+gW0n+xNptTjKnlQI69qdczdwxtZwr2U7wrA4yhs+iWVYVMdBvTrpByCZDKuGHv2RG1qTx+ibruOgvW/o1ofwoGFi3gryo06IgCJTHksa0pLcckpYuqRLAZ08+KK0SO45fI43k/KYtXxUu4z2dC2tQ/QyMQugfy8J485a9IY2ymAWM9Y5IKc1MpURoWPOuscL2ScOnonAPx87Geya7N5rPdj50VOeTpiXR0y1/bD/Q1WGzqzuYWRB1BplMR3jcLdy9H36psmocaNpMMb2X30D3RyD26ceVWbPtd7p0/ANbILOkMptpIM7CYDkkKDWjSTk1dwxvnqXNWMvacX4x/uS5eB3gw7UsyTP0L0Vx+y5o37sFr+3KaqragKu/bc3irytx/CzxBIjVc1Ua+NJv720W22+ylHoDLwDXpH3M6gmLsYEjuLK7vO5s1RPzI6/iHU+i6E+ToM2siwgVwm1lGDG5NTrZjtZ9bDN1CFjpYPZkmSKHvre6o261GoS8hVS8TsKWfflhwOHSrhoNnCtfEBKJTnboaCR03EklDE7V6d2GyO4DJNBmnDBrOzayRBoo16rZxqiwt5v4jsTk1GrpBxa9dQsmxWJr+6iaLS9ssICoLA7Cs6YraKvLbyGBqFhiiPqEsiZbFzRe+EKlMV8w7OO69yylMRLRbE+nrkZ1jR+7q7kVkvI3XlKhLGty9/07tqeeiJe1jywxqy8tK54ZZrm5QmpyMIAo/cdCVW2xXIZUJTWb1nX5iNwLltxq1//2u8Pn+fWJuFfF8XwiuseKasZ4PlfsY+99k5jWG329CWWTGGnD0bo91ipWpFOq6SBzG3DWu3ju2qtAJyitO4SuXCBFkgPsFB9BjWnCtoXHAn6NscxSpJEhtNLqADWZGBGR9+xie3TMPPvbUrydZQQr3aTqTQLGU0bltJ5av3UZupQtB44LdgPt0jwsh4eRemtTn8Ee64tiuHR7Ua72yIQ4dTklXAjaKRV/s43G+RXu50dSunsD6AGdeEcuStErZtTaFPQjcemuFQ0nyYnMu4d7bw0bRuDOoZ2ObYUb4uzBwaxQd/nGBarxASvBLYUbjjT8/xQsO5onfCvOR5NFgbzquc8lSO93bknFd3aP+Vfvjtt+NqNPL7ls3Y28j7cioarZrpt03iqRcfITDk7KkTlAp5C4MpILUqRXg6hnoDy665k+CP36TEPxzV/IVctnU3/XYfJquLD76Lt1JbWdxm3w8+/pEl6zY0/S6XK2jwFBDPsoo2VtaR/vxqPEVfDOEm1N7tPxiT336DeXOeZ+YrDxP6vxfQzryD1V9/3277bQXFVOpcURypwv1IKfuLw3j8x6WIotiqbfGh10AQ8Awcj2SzUXznRLJvf4T6XAUKdzckUzXZ112Pac0OTCNDCLWBMqOWYJmcoNCzu7FaXLPJyvOHCpBJEjfHJ6CQO94+q8wG1tdo0NuLGBKVgMG7AmO6okkG+9CMznw2pSsNkshjvxw84zlmDY8h1EvL80uPEOcZT7mxnDLD3xOJfb5wGvr/OBeKnPJUvG65GQBlUNurLgCNhwfhej0NajWm6up/fE5nsvN1VbVsuvJ6Yg9u5cT4GYxe/QuxvTsBjqCfsPseQWeGnZ+93KpvamYG8oP+FPwisH67Y+WYtncH+ioJhfLMLrTsn7fhggf1Xcwk3N3+W40lv4TJSTsxu3qS99CjmL76hsLwSFw/mUutsW2X0rzkIygtZuTFRm7voOSGHuVsyg7gsre+Jbe8sKmd3VxNVs0K3BsUeMTdQtWbD1C1JQPPASHErF9FbFISIZ8vQFBpKHnpMTpHqzjkoWASSi6Tq/5UvdeqWhNTNhzhDxd4Wu1GYrBH07H3j+/GKvfgoTA3ZDIZAZ10eNQFsD/9cFObUf1CmBTmQ5FoZ8HK9iuQaZRyXry8IydK68nId5zjYi8t6DT0/2FOlVPe0+1ca/T883hOc0Rrmo62/5+rtrSUNLOZCKsVvd//L8HZ2RCg3TedyqIydkyeQVheGgUzH+fyd55v5RrqNHgyeVGuuCzZ1MpXn5JyounnQz9UcTwni8P7HMXG/ULOHNUpZVuoESqIv3ZUu/MT7SK5t94L5noinprNmLtuo/uAvshvuw2vmmo2L1vZqs+aA8ls1ngQXpiPYJOIDfHlhanX8eiwWtIr/fh68zZKa6upNVrI3XIbFqVIdMT9CDIZxuSDyLXg/8Ua5AEOt4zr4B4Ev/MektVE1uQpdLSkUY/ErVYlBU9vo/idfYgW2xmvFeDxbcdJ1sGHbl7cO7Bl3MHhuhqQRG6PcgTRDR3SA4Adm1tG5s6+pTtBMgUvbj7O18uOtXuukQn+jErwY9EOOwLCRZ/J0mno/8OclFPe3fXu8y6nPBVFUBAyd3cM+/a12+bDjz/GqlQycMxl/8KM2g6YKc7M48DU6QSW5VD52EuMevCWdkfQ3zgDr2o7SQs/bPF5blo5ZkUDQ58IBgQWf7qdxEEjHOO38aAzlFdTfiiL1A9W4yZ4IQ87sx8/7/YnsOYexm3STWiGxHLirTswVxXRZ8woyv0DiHjpeRa9/QEWu8MlI0kS72UXo7GaCSuzoRBtDOwRh0Ku4J7LpuOmqmfBPiV9X93KkJd+5fi2SkLTLXgm3oU18xDGTEdaiNNlpS6DuhHy2Xwkcz3GH97Eo3oLqkg35N4abKUGSt7eh632zC64nXKHK0srb71J7aFUgSDjSI3jbSMsOJDawALsye6UlVc2tdPrVCy4uz9BSiWzt2fww6r2V/YvXN4Ru12NhoCLfkPWaej/o7SQU8adfznlqQiCgNuY0dRv2IBoatu1oGr0X8cOO/cUwH8VmWNJ3+Kzsrwi0q65Ds/acoyz32LIrVedcYy+U++hQSNQvWVTi8/l+e5YQ6rpFBlH6FgV7pVBrFiXhkUbiLbazqJX3mlqW3Esh7I5BzAtyMe1UE+pupDI6wfSHhVfL8ew83d0Ay8n8NXHKN/2G9YvtpHZfwR5b95G/DfzyO3dl06ff8zOUaNZ8vLrPDh/IQc8Ahhuq+dwuUBHTR3eng4/uiAIPDnahSivamZ0q2aEZzqXCfvpUFRLasfOnBh/NTajjIA7prQ5H9dB3Qh4bS4ADZt+IHX/b+jv64YmzhN7jYXiN3bTkFzaZt/DeVVUK0CQJAZGto6vuCXcIdn88ERK02cjr+mMTFSw8IeNLdpGhrqx5LEhBCsUPL/pOFl5tW2eM9RLxz3DY6it8SO55HCbbS4WzmroBUHQCIKwWxCEg4IgHBEE4aXGz7sJgrBLEIRkQRD2CoLQ5wxjyAVBOCAIwu9/5+Sd/HVOyikf7fUoSvn5l1OejuuYyxANBgxJSW0e76h1rGRLMzP/0Xmc3Hw8XXWz47k38K6vQHjzA/pe1XZ4/6kolCpKO3jjmZKD1Wpu+tyqMCG3O+7/1IkjqfEtRHssCJ16AoLCm9yUPyg64VillqakIxcUVCsrqAmsodsL01C3I0E9OmYspW88jtwrmJAPZyNTyPHoObLpuP23w5TefwNDnrubE0NH4FdUSNz333LTW68xMj+de2PjqJXp6RTccrP02sETWP3oLbw6/Qamy/YhSgJVEeObjqv9VLje/Hi798FzygjC5i/G7hmI3+af+WPOGsQrInGbEAmiRNVPaRS+vIu67S3lrMdK67HJBL728G0zwnWgXwRyew2ZpubN4j7x3aiPzsee6kpOYWGL9p7uGube0BObAJ/+3r4L584hUbjLIqi0lFJSX95uuwudc1nRm4ERkiR1BboBYwVB6Ae8CbwkSVI34PnG39vjAeDifve5hKg2VTfJKYeEtB0EdL7R9emNoNVSv3lzm8d7T5yIIIrs/u23f2wO5ro6jq93qGHEkmL2Lt/IvuUbWfbA80QnrSOj7yi6j2/9RnF6bvOTuE+8HK9qOxu/+h8AheWliHI7YpXDpy8IAt1Hh1PvVs7Qh+K55vkXAYFVcx31bu11jgeE6C7R8YGJ7Uopi4+kIOQ6KmbVWUtJmTmYI30SybrGsfehvG8ksgnxyNIbyJ94A9GyDBreeJ2ayyeitZh5v0c8r/68HYVo5cbhnds8R2lFBZ3rt3MoYDKeN/9I3N6deI7qgqnISsE1IxCrStq9r/reHcmPDgOg86EDFL1/AGNHL/wf7okqwg2x3krN8kyqVzQ/xEuMjmvvHta+i1EU1LjKW977K68agiRI/PzhNhoMLfPUd433oaNGw+LsMnYmt62I0ijl3NrHITl+97S3sYuJsxp6yUF946/Kxi+p8etk2J47UNhGdwRBCAEmAF/8v2fr5G9hbvLcC0pO2RYytRr9gAHUrlyFaGxdSMK3c2eCTCaOlpdjt519I++vsPTtt/lpx3YAOu1ag/6xWegem0XsmkVkxvdi8GvPtmhfvWQJObfcQlrXbpS+824rg9//ukfIDQ8ld2cCbz79E788m4JbvS9e3Zr/G44bMpgn3ryabvHxBMdF4hnUnaqiI0iShEuAQ8Pu0ePMBbd3lh/llatlbB7mi0puQb2nDkuEhMzfHXtnV8JvfpmQe19CcVM/pBGhCBtz0P7vKdxW/I5NISe1AfYa3BjlayGuQ0Sb53D7pAdawYJL3xsAkLl4EPDRz/jfOpG6dAM5k0diObqn3TlaG/3subdPxM0iUfHufg4V1eA3syuBz/UDARr2NT8sUmoMdKmyo9e1HQNRbTEiCSr0pyW0i4+IpqDLWrRVXsx/eQGiteW/lY/v7IMagScXpbQpHQWY3tWRK39Z6m7yqwztXtOFzDkFTAmCIAf2ATHAXEmSkgRBeBBYIwjCWzgeGAPa6f4e8DhwxkxRgiDcCdwJEBYWdi7TcvIXuBDllO3hOmoU9Rs2YMnNQxPXWlPfvUsXfj9xgqMrVtD5iiv+1nM3lJZy3GYjwm6nQ7feyBP7UCcISBL4xEYwqXvLrJLVv/5G0dNPowwNRdulCxWffYZMp8Nn5l1NbWRyOQ3D78Sc7YW+EqQeZfQb3ImeCSNajFVYbmBjehnrSmso6twfe4cIbiiuRKyzAiBoWq/PRFHkRF4aK/Yt4dvan/AJ9+OGJ5eTV7SO4v2Pku/XgeuHrmpqr9R7EPvU147zrfyEop/eQ5UOtisGMG91CkpJxws3jWx1npOUunUirGIb7gEtlUFej89B8POj5K0vyLj6Roruuo9hM+9AdppUVBYTC0k7SIj3oSQ8BH5Iw++HE6w8Wk6v8TGowt2wZNdS8MIOJKvI06KEHDiQvp+u9/bETd+ctthit3Ft0moQIhnp01KBVV9fwg71arqG1ZCYey0p3y2m223Ne1JhQa7c2T2Ud5NzufWt7cy9ty96XcuUyO5qdwJ0QRSqC/jf70f59IZe7d6XC5Vz2oyVJMne6KIJAfoIgtAJuBt4SJKkUOAh4MvT+wmCMBEolSSpfflE8zk+kySplyRJvXx9fc/W3Mlf4EKVU7aHJiEeAHN628qIThMd1ZsK0k+0efyvYqqq4ru338YukzFs4kQGXHMlfa8eT59p4+h79TiiTzPyksVC6Zw56Hr1InrlCsK++xa3SZdT9t57VHz5VYu2kx6ZDECktpB777yGngktc8isOlRMnwNpPGKqYrWbyMHIQA7H92D9iq2YcqsBKFp1qEUf0Wan6/yuTN10NV/V/cBw+UAWTVmMXudCfPQUCgMS8bMd51jxpjavN2j8TMqmqil8WkXnR96nzCLDX2gg0K/9/Py2Ma9jlwQyl89pdcz9hofwnmRG52cmcO6H/HHFVDJTWm5m1jQafi+9jq5xvtSNDyc1SE2n5GrK5uwj1epQ4EhmO7goKYjS83t3d8KqbGz4LgVb4+pblCQGblnJflskifJc7oju3eI8izc9TY1MxowRHfF0S2dX3hLyVr/Sos29V3Xk8kAvNlXW0v2ldUx7eSOL12e0aNPZtyMenmWsOVLCxrS2N4wvZP6U6kaSpGpgEzAWuAn4tfHQIqCtzdiBwCRBELKBn4ARgiC0H47n5B/lQpVTtoc6OhpBqcSU2raGWePhgcZspqq25m87p6mqiq9ff51StZrxsbFEDGxf1XKS8i++wF5VhffMmQhKJYJMRtArr+A6Zgylc+ZQtWhRU1uFUoGPWER5bdsb4G9kF6OxS8zz8+ePxGjm6BwG7RuvUL738uWXECWpUeGkZFdSUWNCFEW27Fnf1P/9jnN494Z5eHo1G+mR3d7DKgnsOfJku9eg0DUgM8ezJ6eeEzY3ahWtN3mziso5nOnYJI2K68w+91F0KvwFk6GuRTuZXEF2WBcUY1Qcf+4ldGWl1F87g+VvvIPF4ngr0R8+RIl/ICpvxzwH9Q1l9P19sN6eSFGoDn1F84Y1tRYsHTwY2SeEvL6+9M4x8scfDv/9W8d2kkcYI7Q5bBh8eYt9C4Opmi9Kd5JogwG97uFw1E7mRh7h/rzvyH9zCgW/fEZdTjZyhYwPH+jPB2M70tfTlcN1Bp5YdwzTKdr+BK8Eam1FRPjJ+N/vR9vdh7lQOavrRhAEX8AqSVK1IAhaYBTwBg6f/FAchn8E0GrZJUnSU8BTjeMMAx6VJOn6v2nuTv4EF7Kcsj0EpRJ1hw6YjrYfrOIhiuRZLJiqqtC0UXbwXDFVV7P16685WFxCQ6OR733DDWftZ9i/n/KP5uI26XJcBjU/FASlkuB33yHvjjspfv4FZGo17pMclZgCg1UcKvKmNrsIt4jm6N+0igaOucD1Rg1XdnR8HubRkS2LktntpeTbSBV2mQBoICsXskBtlzDL/ZEFvUPWkMGo1a0VKf6u0ZjdhhFUv5FNxz9hWIeZLY7v3fA+glxCrtIhGRxSw37+zQ+i6nojr3+6AE2do8j4sqAEnrhtGtoe09FtWsfBpNV0HT6tqb0kScSUHyDXpwtXXHc1BSOGsOep54j7+nPWb9lMwFNPIplMmFUtXSQA0THeRMd4Y7KLlNabOLq3iE7rColZXQAUII4NJt1PRcyGItYZ7XylrkMta+CrXuNa7TflFe2jRi7nidCJSIKMNNUBAE6oVDzndpgvD/2BNeVFGu5KQh8UyqRhEQzrFcTlr2+iwGrFYhHRNE4xwdvxFjeqi40v1jdQUG0kxLP9YiYXGueyog8ENgqCkALsAdZJkvQ7cAfwtiAIB4FXafSvC4IQJAhC63A7J+eVC11O2R6axESMB5IR28lnM2LkSAxqNd+/8QaGytZpcs+F4pQUPnntNbbX1KBH4soePc/JyNtrayl49FGUwcEEPP98q+OCXE7I3I/Q9elD4RNPYjzkcF9EDHBEjOZsaOmCScqoAGB0RPNq3EWt5vPr+5J8WXeyh3Zhd3wUvwYG8qGLJ0/K9MyQHHnnRYUvI7f/SkZd226F8d3epdquojLnXUrrshx9RBsWq5EKs0Pb3n3gM3SJDEAlWsgsqSHpUDpfL9/Ey+/MRV2bjzIwHpNLEJbCVJ6b/QoLNqWxhd5o93+DLfso9ceTMOQeQhAEityjSSzZicHUQHBgAFd8/RmlL8zGu6QY7e23Enf4IPU+MVhsbW+AauQywtx1jB0Zjfuzzc4CaU8pXW/rQkaAmoTtpcw+6MoEdwsaRcuHxt6U+Xyw5WkAgr1ieeTXqzguF3kuYASxqNir1XCi5/OohQaq13/X1O/md7eTY7XyUM9w3Fyax4z3crgRZVqH5iS1qOVbzIXOWVf0kiSlAN3b+Hwb0LONzwuB8W18vgnH6t/Jv8xJOWX/wP4XrJyyPfSDBlG9aBG5N91M2NdfIdO0XLF2GDOGERmZbCwq5LM3Xufq6TMI6t7tnMYW7Xa2f/EFW3JzkcnlXNW9B52uaF3/VLJYMGdlgSShjo1FkMux5OSQ/8CD2ErLiPhhQbv1bWVaLT533UluUhI1y5eh7dwJ9zBvoIaGWmuLti6SY0VqbOM5LMgFlMgJC3QjLLBljvoXbFau27uZHYZ4Bu8+zp3+x3ixU8u/s0blSkSH2ZSdeJKdSWMwCS64U4dSkFBowFw8nPk7f6LebGGSIGCSu7By8XEEARSo6D58IpOH9UIURRas2Unm8WNIVXnsoBeP131C6ntXsLooDoDeXQNJrE0j2y+aGJUj3kEQBIbOmEbNuDGsW/AzXX5cSU9dFw6+vgvljA50i26/wErS94eJb/zZY3I0Xu5aRt3fm+e/XMBdJ8JJWCbH1NmIRuM417L1j/Fc/ipcJLjWNQZXmzcbDA6f++DuD3Ng4THSXQvJL8qkA1BSdJxgHBvaB+oNDPV2456rO7WYg4/WBz+dH5XWTCCE4yV1jE70b3fOFxrONMX/AeYdbMxO2fvClVO2h9tlYxBfe42ip56i7L338X/yiVZthtw9E69ly1ialMRnS5cQ8vNPXHbllYT2aTeGj8LkZJb9+CPFWi0BNhtX3XILPvHxGPbupfjlV5Cp1SiDg7GVlWFMSUEyO3zGMjc31JGRmI4eRdBoCJ03F22XLme8BsN+h8vA+1ZHGuCqxjQBLl7NDy2bKLK9qh7U4NZGiP+Z0CqU/NpvFOuKTnDPkQI+KQvl8I5lLB7Q8qHVM3wa+5FxNONtsBsoUsbiWVyEi289ydmeWMwWNAJ4qERsGJD7RDB40AAGdo5G2VhxSyaTccO4gXyftI4MUWSUpx6j38Mc/nUdgkIiwk/OnoNF7KEniYldiD1N6+/u4c6Uu29no60b8UVmAuvtWL5IZePwQIaPaVsFFp5noEYpEPRYL0LcHPdMEAReuvVaVn37E13TQtnx7e+MuGsaNquJebmriUXOd9NWo9P7s/Du8fRx92R3xyom/D4Nq6tDrju00LFduLDWhx6N1+aCQI3J2uY8Er0SOVGdRpD7GNJLLrEVvZOLm4zqDBamLWRah2nEesae7+n8JTymTMZ4MJnKb7/FdcwYdD1avWDSadIkQrp0YfvChaRYLHyzfDnDkpMZePvtLTbo8vfuZc+aNRwymZArFIwMCmpqY8nJIf+eexE0GuSRkRgPHULu4YHn9GvQdu2KZLfTkJSENScXjxnT8b7tNpT+Z1/VyT08ABAafdJuYX4IUg2Fh0vpBPyaUsgzBSVUaQQS6mBoeOsi2efC6MAYDvqFMXrrCraZwtlamslgv5b53nuET6VH+NSm37ckbWDz2g3Y7Upm3nEbAaFnlzaXHE8nQ5LoIkn0eug5SpP3k79sN4l+IYx9fx5z33kB854D2GNbRwybrXa2fH2QhCIzh6P19JkSx7H5h4neWERGjBfRUa2v3SITyItxoaNby7c5mUzGhFuuZf1nPxOfGUTy7l0Um9dRIId3o6ejcw3EmL6D/Go5tyb0IVRj4xdTczpoEfjINoWf7CP48YVVWHt4I44NoqrAQGm1AT+Plj74BO8EthRsobO/irSSei4mnIb+EkaSJObsmYNOcXHIKc+E36OPUbdhAyVvvE7kzz+32cYjIoIJjz/OwOxsfv74YzYUFpL93HPExcVRXVFBanExVVotgigRJYpcfscdeEREACCJIoVPPoUERMz/DlU7sRzul7ddzPtMCArHfzOpMVjHq2MEIaptZFT7UHgsn0+zy6lyFXhR4cZt48ORy/96CiqtXMWbHbtz5eFqfsk/3srQn87gPiNYvmk1SiXnZOQB9qxcAZLEkGscm/qbFzj0+GGDhiDIZKQHdyZszwHqxdbmZc3vafTIbCClhydjr0pEJpOReHMnyt47gPnHYwQ93Butttl3lZFbhd4m0U4sEwADr5/I0TfWY1ttZ13kOrSixLC+DwGQvX0lEgKRQydxpbuGX7Y2G/pu+ltpqHRU1BLMIv5mKAJsQVoa2tg7SPBKQJRE/Hwq2Z2hwmoXUf4//lb/JhfHLJ38JbYWbGV74XZmdp15Ucgpz4TcRY/L0KFYMrPOKm3ziIjg9pdfpp+LC5lyBSszM9lRU4NMgiHe3jxw+23c8MorTUYeoPKrrzAeOID/k0+2a+T//zTPu++NvVjTw50eReUcdIXQepGZg6OaXCT/H2rMjtWmq/LsqhBBENB4a5DVy9qNDD2dzOIS/Boa8O7kiAGI7urYqsve5Ygifmn8GAx6N9K/msvu48eb+pU1mIndV8HxYA3jpnVsetMK8tRTPymCgDo7m350bFiLosjaNemYPj9CrUoganj7fxOtTo+1h41ggy9VNcFM1UeiUGqQbFZWrkoGwDMskfiwbqhszSbP2BCPpJbjFeRCr37BvNInCiSJnnYFkT6t91xOKm/UukIsdpHMsvbLEl5oOFf0lyinyilnxM8439P5W1B4eCDW1SEZjQi6MxsxuVLJ2EcfpXdGBqaaGrSennhFts7vLkkSVT/+SOlbb+N62WW4T/57I2wBbKWlIAgovJrdEr694thV31zp6N3OEX/b+daVFQD+jPALOaf2gX6BlOaXklmWSYz/maOljWVlVKpV9D5Fytrj+pvJWL+aE3lZNBQW4hEURK8Hn2bvu6+w/uWnyb31Xq4aNoxNa0/Q3waqkeGt9ooG9gxh9fEqOh+s5vdlqUhGO90PVJHtpSR4RgKRZ6lE1WvUaDKSNvJQaW86zXoQgO/uvrb5njz2IJO++p4nwuawdt3LKKJ8WO0diTnai2XD4ghQ6+m8+gCCUuDjfm27OP11/nhpvDCQC4SRWlRLXMAZA/4vGJwr+kuUhWkLL0o55ZnQD3YoSWpXrTpLy2a8o6MJ7tEDj6AgRKMRW2UlptRU6jZtonj2bDJGj6Fk9v/QDxlM0Jw3/5HNamtJMQofH4RT0gCUW5o3/IqHd2NQxF/zy7fFtqo65GI9Q30jzql9dLCjiMeRnCNnaQl5hw6BIBB02ltPr+k3YpPLSP35BwDGdenE1bPnYNa5kPXJ2yxYuoNeuyvJCNYSldB25HtipEM103FHMQEHMjkWqmXAI33OaOQbqusoOJqNTOuCt2s5noY4im4cTe7l/XDLckgh3e0SpfWOoLoQOTwh5fGIbQ/XdfwFtAp6JWUQsiWFKp2cq/QuhLm1jkUAx9tPglcCBYZ0VHIZqUVtpze+EHGu6C9Bqk3VzEu+OOWUZ0LXpzeaxESKZ/8PQaHAbdKkMxpmS04O1UuWUL9+PeY20iQIOh36vn3xuftu3KdMblUs4+9CrG9AUCqRpOYCJh6nVKFau20X3eNi8PVtX2J4rlSZ68mTgumoqmg3u+XpJIQmsJOd5BXnnbGdKIpsXrsWuUJB5ID+LY75du8BX4OxsqLps7jQEGY89wqbHn+CoTsd9QPipsa1+ptZ6kyc+CoJtyIZBsHCAXkmx+VFXB7uh+wsPvCl3yzmeHUOnjIXOpj96KhwxXi0EIVeRi8PPeMefJBdS1exLyuNurxcQna8SJi+ErECepiTMXqm8oPR4ZLxMYi8P/TMbzTxXvF8e+RbYvzVpBZfPMobp6G/BJl3cB711vqLUk55JgRBIPTLL8ifdQ+FTzxJ9S+/4n3H7ajCw1EGBiIoldiqqqhdtYrqxYsxH00FQUDXty8+Yy5D0KiRqTUo/P2Ru7qg7dGjlS7/n0DTqSN1a9Zgyc5G3eg+UslkJJQUkuofxI1WDbq96Xzvl8+Ant3+X+c6UFWEJNPSy+3MladOJdQ3FJvMRnlh+/nWDRkZLH7hRQpiohnu64tHeMsMmlJjIRjhNGlobIA/SapmN4/x/QOkKuXIRQmrqwqTpxWXXCMuopLCIAOdbhpIoLknlZ9/y9I9q7EKNnpPGNTmnLb+uI7j1Y50zGq5iiRlJgbJzNT9LTOi6/9YSx/vPGo/GUWY0pERszYkisvH7ODVbc1FST7vHnXWh2OCdwI2yUawXy3JGee2p3Eh4DT0lxiXgpzyTCg8PQn/fj7VixZR9u575N1xZ/NBuRwaDY46MQH/p5/CZcRIVCHB52m2DuzlDgMqOy3kf/nEoRQWl5BdXMqTVQauLZMxbsFv3BgfRd/unc95RX4q3TyDQDrC92UKrqsqoLPn2a9dEASsgVbkBXIyCjOIDopu1Wb/E0+Q2akT7iYTg2fObHXcWOUo16fRtnzAyOUKjnvLiKk/iofKF7uHP3KzhAhoq0241wjU25XsD6hj8v0Tm/rd+tBdfPvu56ze8wchHSIIjG2537Dn921sSNuOFhXTp1yNxl7Hx8uWoHJrndI6pGoNgX7Z1Np1VLl64FlXjUdeJit/mERB8MsgQA+rnP5BHgCYLDYqDM2R2LLGojMymYCfynFv5NoCyuoiKK834+OiPus9Pt84Df0lxpy9l4ac8kwIcjme06fjNmECpqOpWAsKsBYVIlmtyF1c0PXvj7Zjx7MP9C9gLSmhatFiXEaORBnc0ui6uOjpEBPl+MrJ45Wte1npG8RvtRJdfl7JR70T6RBzZnnk6Xip9bwUBi/maLj3UDKbh5zbQ+76cdez6ItFzP9sPjNmzECj0XDo0CFGjRqFRqPhcJ8+KIrzCQREqwWZvKVBtzUGlMmVrfPXXHHnvWx64THkoo0gtzFMf/k+BEHA1GBh4/xNZB62IWUpaKgzond1jKvWaZh2w3Q+/fIzFv20kJmP3oNKq6Y8rZDSohIKchx5d264/gYCIgM59OQAntFnINSK2J58CUvoFHT3fIFYcoJAl2yMxOA2ey95x97GuOlN/EqtjM/YxqaiW5gdNZPHhl5LRlk9z/52mF2ZFbSv65Jw6aBhY1YyEMH6oyVM73Php1UXLsQsbL169ZL27t17vqdx0bElfwv3bLiHx3o9xo0dbzzf03ECFDz6GHVr1xK14ndUoaFnbV9VVc2CTTt5T+WGTS7nE73E2MH9z9rvdAZt/JkMMYLjQ7rj2obxPZWSqhJeW/saOcU5dK3q2uKYq6srAwYMYMvnH6JocGw+qm12dDYRUanEy82DDgOH4Ornz+L5n+Gv0HDN59+h1OtbneeTux+ioTIdvVccMz9+u+nzPZuOsvunYlBamXBfZyI6BDUdO7xuL4u3/06Ixg+7aKfIUtFizCvCZfiXLCfIdByDTQfajshqs9BoyzH0fxdV9lIURZsw+VyG5t6FTf1+3vI51/zxKABL3UbysXQPx8oc9Ym99Sq6h3kg0CyIbTaTEqm8iR0rJcfu5LExHbhnxIXx5iwIwj5JktpMlu9c0V8inJRThruFXzJyyosdw9691P7+Oz6z7j4nIw/g6enBvVPGcXluPtfsPsqTRgWDGgy46FvKSTMaTHyeX8bLscEo2nDxTAsM4LUiNZ9lHOCR+L5tnstmt/Hq6ldZVrIMs9xMJ/9OjBg1gm1Lt2GxWNBqtdTV1bH+t8XoGo38kEGjyErei1USwWIlv7aSrLXLAFCIIiV2M7veeJnBs19rdb5eE6ey+bvXaahMw26zIW8MJOs9LBFDnYlDKypZ/u5hBt1QS8bBIjz99YyY0pvU48c4UubYTB8Y2RO1Ws3+YwcJUNXQPedDAHIiZ6AZdRuVP04jRF+DRoSkA+8y9K718H4HFAV/INZWIHNzJIy7euAt7DqSTL+S72k4XM4g48e4ePXkltuvY0L3M6/Q5+zpy89pPxPoruR46cURIes09JcIJ+WUH4748JKRU17sFD79DADed9zxp/uGh4Xwcl4h11tUxOx2BB3NDPHl+ehAZDIZ1xzMIN9s5Wi9iWU9W68ob4/pyxv5u1lcXMMj8S2P5ZTmsGT7EhZWLqRWUUu4PJzHBzzOkFiHQmtg/EDq6upwd3fnt99+48i+5rfr3vc9yKmlPaz1dWStXklVXi7H9+yk1G5h/5FkBlityE+rKtVrwiB2Lo7CYixm/nO7mPZEL/Qejs3woZf3oK5qBzk7TOz4ziGLLKEerUsKV826jhPPvY+2OhKpqxdDpgxkCGMo2r0UVkJW18eInPIs2W+GkWCooUom5yt3VxbpbHS3WVHFPogu/T1Me1YjxXaiatUc3AvW009mJM/gTlbgKCyV5fQv387+t1MoGXEF118/FZWq7f9HCd4JmO1mOgQZOFJ4cUgsnTr6S4CTcsp+gf0YGtK6WLWT84M1Nxe5jw8y7bkrYE5l1MA+eNY3G5JP8suI3nqIrtsPk2926PB31zawoaK5zcG6Bq7cn86HuZUEy+vItvtRZWneoFy+ZzlfzvuSqoNVRFZHclPgTSy7YVmTkQeQy+V4eHggCAJXXnklz73yKppgxyo382jLSlFKF1c6XHUNfR96jOu+X0yEfzA2hZwvH7wNY311i7ZZyelYDJnI1Z1oqLLw3TM7SVqeicnguJaJNw6g62QvYobq6Xa5Q2t/Yn8xFoMZ1/JOKG2uHF9jZO3CJOw2O0qFw3xJWm9OHPqBCEMNOzpNxOPZcroP/pQClYJ7v5lGelpjgbutj6L9aggBhcupFnzI7fgoAS8c5plXnmf2xx/Q8bYnsGlcqFrzPa/dcRsbt7Zd8zbRKxEAD49SMsvqMVrs5/YHPY84Df0lwEk55eO9H7+k5JQXO96334a9spLK7+a3m0//bFS5OFISf5QQxnWBXlgliVKLjTidmmXdYxCAWUdzmtrfeiibHTUNvJdTSh7BSDI1L6Xux2A28N4v77F75W5EQcQitxBdF01wRTBWa+tsjbt27eJ///sfGzc65IfTHnkaSSZj9Rfz2p2rTCbjyvc/wT8mjLrySj69Zzqrv3ycyiJHNajtCx0unpG3jGPg1BhEUWLvimy+fHgr3zy5jU0LjtG5Zwcum9GXgRM6I3c3UpetZO7TyxEaTZWAjPQ/GnjmxY84vucnAOQh4XgsfYAipYquY95AkMno3msCT+muJPiEC3mFZQBIdisZHmOpnr6W4JcOEzbtOZT65pTPY8cM5oXPPiXs6lkIoo1d817ljy27W11nuFs4WoUWVPmIEhwrvvBX9c7N2IucjOoMpi6bylUdruLZfs+e7+k4OQV7TQ35996HYc8eXIYNI+TjeX/qQVxuttBph6O6VvHwbm22GbH7GEcbTAz1dCFEo2JBUSWJeg03BHmzvqKGPyqq8DMWMe7gDjQWDRYvC9decS3hXuF8/PHHGAwGBEGgQ4cOTJo0Cb1ez6+//kpKSgoAHh4ePPjggwDMf+0lSpL3cP3bHxMQ0vaeg9Vi4efXZ1ByxIzO34ChRIsgg7AeARQctmC3mnlw/o/I5HLMBispG/NJ31NCdYmhacNTpVMQ3MGD6F7e7PwxmYYGLVZNGQOiY0nfthu1pZrFfXOZb1qA3CZQrVCgFWzkXf0V1RZ31i/5FtFsRZvhCGhyTQzm8j4D8RkxDaX63N6uMrML+P65x1DYzNz/xfxWeyQ3rLwBmx12bp/O/67oyA39I85p3H8S52bsJcxJOeWsbrPO91ScnIbc3Z2w776l4osvKHv7HSq/+hrv22499/4GAwA3Vhe322ZuYjhTk0+wucqxKSgA7yeE0dlVxy0hvoz+/heOBEZgFdT0vawrU/pPaer76KOPsmXLFnbu3ElaWhpvv/02ISEh5ObmotPpkCSJ2trapojenqPHsSp5D3vXrWbiLa33HfYs+5UtP3wNkoR7RB2Rl+WjMl5G0dFqcvY6ApU8w3ohawyqUuuU9J4QSe8JkYg2kWNJxRzdXkhZbh1ZyeVkJZcDDsMcWGHCbe9sepSnARD/0idU/vwLlb94IcgkUkaoibS7suXVd5DJJUS10Hg34I7nP/nTb7reXu6ICjVyawM2W2vXTIJ3AktPLCXcW8PylKILwtCfCaehv4jZmr+V7QXbebTXo3hp/r5cKU7+PgRBwPu22zAmH6Tso49wu3wiSj+/c+rr5uaKymLhmNHC1t0HiAgKIDQksEWbBBctRwd1ZndNPck1Bsb6uhOmbQ7guTY6iKdMcnyGXc6U/i0XezKZjGHDhjF06FA2btzIli1byM3NxdXVlfvuu4+FCxdy4sQJSktL8ff3J75HL1apNOQk78NRSbQle5b/ApJE55GXYXVdAYBFu4bgriNxicqgJkekOieeypJ8vPxbBkDJFDISBwaRONAhrcw7WsnBP3Ip3J+NylJHfPIHiK7+nDTXkX4x5P7i+DcviQKd11vYnvMGMjcVg68dysAJT3AgdTsymexPG/n6BiPvPPEkLoZyQqfehYd768RlCV4J/Gj7kcndlHy6oZITpXXE+F24Cc6cPvqLFKtoZc5eh5zy2vhrz97ByXlDkMnwe9iRH73oySfPmmb5JHK5nCvLC9gdGMa0BoHe6SXc9P1vZGTltmrbx92FO8P8Whh5gJv69sXDbGRVdeuI0ab5CQIjRoygc+fOdOnShQceeACVSkVwY4BXdnY24HgweETGYCwpxGxqOV7+sSMYa2vwDY9kzJ33MWHGaoT6HgCYFBtQe5bj07Ea7G4sen0fuWktN3VPx01RR+DiFxm84ym6FvxCzMoVJO5cycaJjgfEppfvbdXHHiwSd2UWBtXn7Nv6Kt0TBtI17s/FIIiiyFtPPYtbZTZ+E25i+tUT22yX6O3YkI0OqUYpF/gh6cx5gs43TkN/kbIwbSFZNVmXVHbKSxl1dDR+Dz1Iw46dlL711jkb+3dmXMEaPw1fygzcVpzNRt9gRhwv4tNla84pf7xMJmOIUiJdrSe7qvqMbadOncqVV16JolHfnpjoMGaHDjUXMQ+KjUOQRPIzmpPE2W02lrwxG4ARtzanRxg6/kc6hH5NXOSnlBwIIHNNMAOmNyCJApt/3t/uPGxmKxm334uuOA3D0Kvpum4x+rAAAG57eQkZPfyJWn+MjDEJFAyKZVMXWDES+l3egMbTAqKcauuXbF/756PDf126DteSNNQDruCmG69qt12URxRKmZL8hhNc1jGAxfvyMFkvXPWN09BfhDjllBcnnjfcgPvUK6n88iuyr5pG8ezZ1CxdesY+MpmMrh3jmTB0AK/MmMzWhCC6VpTwgqs/1/2wjM1J+9pUzZzKbXERSAi8sv/on5qvj48PMpmM4uLmPQJ3X4fbqbqslPrqSr647zbev34KZkMDiUNGEBLfnHpCrlAQGjuEkMhR2KpDqC9wwcPHlw4DLdQWhpCd2trYSzYbaUNHoS09genKe+jxyYvIFM0eZrVGz/BPfsWsBH1qLrreQ4m51p3uUy2Ua8sJtUbSo6cjjbVJsZpjh/445+u12+wcXvoj9WpPbp950xnbKmVKOnh24GjlUa7rG06tycaKlKJzPte/jdPQX4R8fPDjSzI75aWOIAgEvvwyPrNmYTp6lKqffnZk4Vyy5JzHCA8L4bdrJvBIRT7b/IO5xiAnYe1ublqwhKD1+0jceogDNS0rH/UNCSLBXM8267n/W6moqODdd99FFEW8vb2bPnf3dujbayvKWTT7aWpKS9B7eNFx2CjG3v1gm2Nt/fU1yo43ENHPl/D4YfQdOxKZwsSe1a3dN/bqamTVjuLpXV+e1ea/b72bF0VX9CEgrwHPd7/A/ajjQdDZdza6Ti+zd/94AMrLQ1i+dC0Wi7nd6zz1zeqX31bjaiwnZuxUNOozp40Ax4ZsakUqfSM9ifLVsyAp56x9zhfOzdiLjMzqTH5O+5lpHabRwbPD+Z6Okz+JIAj43n8fPnfPBEki9/Y7KHr6GeSurriOHHlOY8jlch67aiK3VVaxfu9BNlRVsTTYkf640mZn3P50AlQKamx2/FRKPogPpQ4BlXR2V4/FYmHJkiUcPepY/cfFxXHVVc0uDFcPRxEQQ10dlQWOxGJXv/gangFBrcaqKstg7ZcvkH+gGq9IBZPumQuA3s2T4M7V5B/0p6o8F0+f5pQDCh8fjAGxaIvTMRWUoAsNbDUuwLiXv+XIhN+R3fIYyn1l0A8O1m1DVvY8CgUcPDiG2hpH4faXXnmTXkMuY8rIPk39jXV1bP3xGw5tWINcqSQ4vjd55bWICEy7asJZ7xM4NmQXH19MkaGIa/uE8fKKVFKLakkIdDt7538Z54r+IuPNvW865ZSXAIJSiaBSEfrJx2gSEih65lnMmVl/agwvL0+uHjOMT6+fwlMYmbl4PvNef4YIq5lSiw2TKJFjsnDFgRPka1y4wvXMq9TaegODXlzCe8k2atBx1VVXMWPGDJSnpDIwNUo+FUolQ667BYAjm9a3Guvw9p/49pF7KThYRcygYK598TuUqmYteq/R/ZBEJduXbG7VV9S4YlZ7oAn2P+N8O/afiMwjCIXZob6RmdY6+osy6mqbq1hpkHhmXSHXvvIT5ZW1mA0NzLt9Boc2rAHAbrWSe2gHFB0GuUe7qQ9O5+SGbGpFKlf1DEGlkPFDUuuN8gsB54r+IsIpp7z0kOl0BL01h5zrb6DgoYeIWrrkL43zwPD+HPNxo+q223j32Qfw+uor4jolsq+mgVs2JYEED49pX4EiiiKPf7uRUtGRdXKZyZWUX4/zcIOcCX0TmtodbSwAHtO1B+rGFARJvy0kvGsiBmMeZTnHKDx+lPzkSrReApc/+AwhsQNanS8oKorATpvJ2RtOSvwOugxqbqOKjUWZvZ9NIwajVNkpj/Ki2+2PEdFzWCtXjmQ2oCICcLh7soo7EJUzhluNPTHKJOqkWnwld2rcyvilRs+o11fz0539mvp7BHWlrjwHu6UauXYkKmU49QYrLrqzG/tYz1jkgpyjFUcZFT6KiZ0D+e1AAU+Oi0evvrBMq3NFf5FwUk4Z5hrmlFNeYqgjI/G+9VbMaWnYysr+8jjxnTvi9dVXAFTeeisHtifR013P6IwUxuan4aFrjgo1mq2s3ZPKlDeW8Oqibcz8eDWr8yBCb2PFzB4MD1NRYJTz+NK0pj52m43MnVuRubgRmdiRkPiO9JkyDYCFL77I7298SdJP2yk4VEFwV0+ue/mTNo38ScbfOgmddxErF1ZxIr35bSb+1cfZ1C0YfU0lHvk1xG/KwnT9LHYO6MK6u6+kttpxj/IeeB/JWI0mKIJfq5TszI6jMK0fYn0gm1Vp7PUx4i95IEPgpfsnMm9cIAaUzPpiMzKF44FWU5KB3VKNi09HfEYOQyb34NDR9ittnYparibaI5rUSkdFq2v7hlFvtrH8YOE59f83ubAeO07a5aSc8oPhHzjllJcg2u7dAWhI2o37xHPzEbdFXKdEjn/1NWW3347mtptZ0q0nrvExWLWuzP78F77KcGSLFJCQEAAlB/Y5CmcnutuYP2sU3u56vpgVyIOfr2ZJhh2jxYZWpWD7yuVIxgY6XnF1U/WrwdNvYt+Kn1C5Whlw9XT8w7viG9IFhfLsVZc0Om/Gz+zJsI9O8MmXR3ltiI0pIyMpr69j3rgSBo4fx8fXv0VRQRoHfv0cNu0kamMqBf2GsDXWhah0RzRw6Nz/Ef2dO7V5JuRRMnxG9Wfd94twqykmQeiBr+SGTq9i3LBerN2Vxm/VXlQiJyFmFNe+8iD11XW4eLiSllHJ+u1VHD5YQv9ebe8NnE6CVwJbC7YiSRI9wz2J83flh925F1wxEueK/iLgVDnlsNBh53s6Tv4BtN26ovDzo27Nmv/3WB06JdBx1QrSb72Tw2qHYkZprMOY3yyvvCxMzkP9vdj0YH8e7ufBXT3cWPLoBLzdmwuGBHs4fOrv/upw1xxYtQxUGoZPvaapTX1NAXarjPCuneg27HYCI3ufk5E/iXdQB4yN7pinthyn0/NrGP+LQ/9eqjSTX1tDUGgCEx54hwm/7cTwkKOgTliGw8j/OFTG0dzDKPKV6OUanr7+aboFh3Pz1dehlpSsViVzmAYEQcBuMnO8yIwgSWgU0QjlDveni4cjojUu2osGOZRlnnuSsgTvBCpNlZQZyxAEgWv7hpGSX8Oh/JpzHuPfwLmivwhwyikvfQSZDG337piPH/9bxvPw9GDiow/w3sPz8W1oYNaMkXSPC8O28jiL9uVTp/LkgSscvur7Jw9sc4yZE/ryzYFV/H6smmmHDmKrLCN0wDBU6mZDnpGyFiSB8E692xzjbCjkMoZFlbEp05fBgSIuojcZ1n4UkUa6ZQvjfhuJUvRDxIICPTq1B4rHe/DyoCcwVebzW9oTrN50J4Plg7mu9zTkjXl0EiIDsQ8ezOLt61ghL2XZk1+xwe6FWevPjZoyBkV25nCxD1lr9hF5Wc+m+Yg+KlRlZkRRPKeavaduyPrp/JjSI5jXVx3jh905vBbS5S/dk38C54r+AueknPKq2KuccspLGEkUsZWWItn/vujKXRv2kKnxYWxUIEN7xOGm1/LG1M6EeGrZfqKC2cuPnLG/0WTCIMrpGaJny+KfkBAYPs1RvayiqoYDKWmk7HAEfEV2HvWX5/nRDVfjo63lWE0tcx/ox9qHHiXlxhRe7vMJcfpRuCp88VZFoZZrqRWzKZen8NXR1YzsN57Pu36MSW4kKWgNkYktZY1lCz4HoKa+mlWSL7HWKq7TVjDrtrH0vX8saksNe3491qJPYKwHWlEgJfXc/PRxnnEICBytdLwtuWmUXN41kKXJhdSZzhzI9m/iXNFf4DQV++5+6Rb7dgINO3diPHAA/6ef+tvG/OGPI2htHlw1vVmfL5PJWHHfYAa8sYGvtmezM7OCb2/tg5+rplX/VftOICEjMdCN/I1ZaBH46PmXEEQb+oYyZI0VVS0yJVM+WYFaIeKtl3NdvzhGdu53TitiABetjrsHa/jfWhVDX/+J2wb6cvPQ0VyRMJArElq/bfT8+jIOlicB0K/bIEYmwXqNgPTNBHiueVO3pt7h3vng4SvQeXmhcm2ZdCzMz0JGpQ8VRzI59ts+PIJc6BoeznZgx4YsunU8e/I5nVJHuFs4qRWpTZ9d1zechXvzWZJcyA39ws/pHvzTOFf0FzBb87eyrWAbd3W9yymnvMRRhYWBTIat7NxWkmejpqKG9VYPRimrcfV0b3HMXadk06PDiA9wJbWojv6v/cHjiw9SWd9cHCU1p4g5G3Nxl1vJKqtnvc9w6kK7gkqDpHFB1m0kvhNvQTngMqp69kGlAKsoZ0++C3f8WMXQ17/nm00rsdhs5zTfW4ZNYtaAGuyilf+t/r/2zjs8qir9458zk0ky6cmkAemhTShSQhHpTcSG4KKgro2fq6i7srjrqmtbdy0Ud+3YQWkCYkFFBSlSpHcINSEhoaWQXmfm/P6YSUjIpGfSOJ/nmYeZe++5972Hm3fOvOd73jefxNSqlStdvPpT6HSS1DxrHLxvO+tCqHhdHpeObys7LuZha96dr9+fx4nVP3Fh61bMuZdrvIb3C8Wi0bH07dPsSzawYYcLW5Zb0z04bz9ESVHtisUYDcYy5Q1AzxBvurX3YtG2xFrnNHI0qvBIC6XEUsKk7yZhtpj55tZvlNLmKiD5iRnkbd1Kx3Xr0Hq419ygGj5+dyX/PuPC0jEBDCy3IvRKPtuSwKs/HqXYfHnVrJNGoJEmpIRP7+7Jo0v24aczs/6FiTXOEeUWZLN4yxq+2J7LmRx/PHT5OGnMFJu1+OkL6NmuhDsGGBlirDziN5vNPL9iBYv2evDGbTomDhhr9xof7lzD20f+yoOdX+SJayeRX3CJ2xYNwstiYdnZ8yQGjyf0wfkUZecy7/XXyXa7vFBLazIRCYT7+5Jz8iTHLoFGF82NEwYhda5cOppE2uE4gvf8wPkJkxj/4owa+3r+ofnM3T2XjXdsLBuQLd6exDNfH2Tl9EH0CfOt8RyNQXWFR9SIvoWislNefRjuvw9LdjZZK79q8LmWncghqjCd/iPs/t2Xcf91kRx9+XpeuqUbvcN86BXqg4eLlmKp5cbO7uyLP0e2WcftfTrUSgjgoffiodGT2PCPu5h9i5mBYVkMDC9gTOd8Aj1KWHfKl3u/uMToWfP5aO0y8gqzWLN/E3+eP48B/17Kor0euOsKGdGt6sndqT2HIM2urEnYAICb3pdpHSdy1MWZgy7ORJz/kZT503AzGHjitdd49N57uXPYMEZFd8SnpIhziXHs+H09calnsJjOYM5fj3eknk439qH/zAmM+/gpzrZrT/CKBaQcP13jPRsN1gVlR9Mvx/tv6dUed2cti7a1jJWyNY7ohRCuwG+AC9aY/gop5QtCiF7APMAVMAHTpZQ7rmgbCnwOBAMW4EMp5Zs1GXW1j+izirIYv3I8RoORj8Z8pJQ2VxGn774b09lzRP34AxrXynHz2rB7y34mrUrm78F5TH9icp3bm0xmejz3PR294XyedaT/+wu34OSkrZc95cnOz2bBb2tZujuflBxfNMKMRVrP27tdOgMivXlw+BACvKoPVQ5b8ACXzMfYd98mNBoNqRcPMXL1FP4RNIxrT54lKu1XEg3DcOp7D/69b+LnRQs4sWkdFOZb1w+46rnrn/9GbzKx4LknCdS7c+fny8r+1hIPneDSHZM40/Eabv72i2ptySrKYvDSwfylz1+Y1mNa2fZnvz7Iit3J7HhmNN61WGnbUBo6oi8CRkoprwF6AeOEEAOBWcBLUspewPO2z1diAmZKKY3AQOBRIURM3W/h6uK9farY99WK/5/+RMnZsyTcOoG8rVvrFeNduHovzuYS7rxzZL1scHLS0idQy8FMJ1JLnJnQ3dAoTh7Ay82Lx8dNZPMzd/HBlAD0Ttb7c3fR8sbUifzjlltrdPIA/YIGILXZbE2yxsZTM6z58T1dvAl7cAGnA0bTIX0z55c8zzv3/YETa75H6+xMlxsmcP9bH/PkgmW069QZH2MM13TpztniAk59c/mXVHj3TiTfPIWOx3axaf7Kam3xdvGmg0cHjmZUVPBMHRBGkcnCV3uS69RHjqBGRy+tlM5g6GwvaXuV6pm8gUqzJ1LKc1LKPbb3OUAc0KER7G6zKDnl1Y3HkCGEffoJ0mIh6YEHib/5Zs48+hgXZs+mwFawuzpys/P4udCLkSIdv2D/etvx6NjuZe+nje1V7/NUhRCC66/pz+GXb2VKv1DyisyM+e9vrD1SdX3c8gwLt4Z2NibuQVosfLjrDfQWC4N73oeT3pOIR7+Cv51kS3o0AD1GDuPPH3zOTfdNwxBUMVnaoL8/i9ZiYc83KypsH/viXznr1x7x9hxyLlW/iCrGEFNBeQPQrb03vUJ9WLwjqdknZWsVoxdCaIUQ+7BmDlojpdwOPAHMFkKcAeYA1erChBARQG9gexX7HxJC7BJC7EptQL6P1s7sXbPRO+mVnPIqxn3QIKJWfUfwSy+hCwqmJCmJjAWfc3ryHZz/18vVau1XfPkr+U4uTB3WpUE2DOoWwX9vDufpoQEE+To27e6rk3ry6m09MEvJtM9387811kVjP+7ZwD+/nM/iTatIyzzD2oO/8+yXi5i58As+32FdQawvPM1DC/rxq/kSf9RH4GfoVHZeJ3c/7nnJWvlKn36U/IsnK1XlsphM7P/0Q8waDWfzKzpzZ1cX/P75PIa8S6x76t/V3oPRz0hSThI5xTkVtk8dEMbJi7nsSMioX+c0EnVS3QghfICvgceBh4CNUsqvhBCTgYeklHZXTQghPICNwH+klNX/DuLqjdFvSt7E9F+n82Tsk9zbrfoKN4qrC3NmJqnvvMulhQvxvvVW2v3n3winystgxs9YQK5Fw4b/3lVrHXtLYW/SJaZ8tI3CEjNdfU9w9FLlX7RaYUYrzIig73D22YVWSlwlPBI8mKmj5qJzrqxWWjH9JhLTre+j+npy29+XUJyby8aXn+f4yTgKnbT4o2XAhMl0nXJXpfbf3vM40bvWwfuf0X24fQXT5pTNPLL2ET69/lP6BV+eSC4oNtP/lbWM7BrIm3f2rmfP1I5GU91IKTOBDcA44F6g1GkvB+z2gBBCB3wFLKqNk79aKbGUMGfXHJWdUmEXrY8Pwf98Fv8/P07Wt9+S9OA0TGkVNfcHdsVxxMWfSSHaVufkAXqH+bL5qRH0DTrAicwobopax/JpIbx3hx8P9M/l1ZvM7Hx6IPtfGMfkqABcLRZiC4v46UwKE1N2YMk5bfe8N770PmOCrb8SLqVlc+qLBXx272QOnD6On96DIdcOZ8oni+w6eYChs18k18Wds8+/gKnE/rqArn5dASqFb/TOWib1CWH1wfNk5NVOl+8IalwZK4QIAEqklJlCCD0wGngda0x+GFbHPxI4YaetAD4B4qSUbzSi3W2OZceWEZ8Vr7JTKqolYPp0dO3bc/6FF0mYdDvhny/AOdy6+nLhdztwMvswZfLw5jWyAfh7uLL40Sf4Zu0N+DqfxUc/jX4dr2F874q59F++eRb/zJhG0e63KHbfgUfCcUo+HsEl43C0gT0Rrj6IDbOQWi0gKLFY8/P0Hn89695YSK7ehSEDh9N/xpM12uQbbCB/2mOEvvMqa157jxue+3Nlu/X+BLoFVlg4VcrUAWHM33qaFbvP8NDQ6Pp1TAOpzdd+O2C9EOIAsBNrjP574P+AuUKI/cArWEM5CCHaCyF+tLW9DrgHGCmE2Gd7jW/0u2jlZBVl8d6+9xjQboDKTqmoEZ8JE4hYshhZXEzSAw9ScuEi+Vk5/JjnzgjSCAyvXNavNeHi7MZ1/RdSjDMLjn7Cw78v5P7t35FTbK1u9du5OMb8toonTx3CdN2L+N+zg4IJr2F2dsJ3z894/TQbz2+exSMzC8/0DHSXMtmWFYp3qIVrhk6ng4s7Qkq2bF3P7nffqpVNw6ffTXx4NwK//JRzp+xr42P8Kk/IAnQO8qRfhC+LtydhsTTPpKxaGdsCeG3Hayw5uoRlNy2ji1/DJtEUVw8FBw+RdO+9CFdXPu5xC8t8uzOzpyeP3jkEjab1y3KD1+8DQCeLMaHFqD1DZ+ccvinsUXaMnnxWdjfQO8D6d1OSlUhB6nbIPoswFeHe5y8c3f0Nq99YyNAHr6ff2McBuLhzO9/MmUuh+2g6+FqYNPfhGu2J33eUnKl/ICm6Jzd++0Wl8Nh7+97jgwMf8PuU33HTuVXY983eFJ74ch+Lpg3guo71V0NVR3UxepXUrJmJz4xn6dGlTOo0STl5RZ3Q9+hO+JLFnHv+eZb5WuWQcw/ksL9kFw8OjqJvuC/OTpedUYnZwqGULLbFZ3AwJZOCYjPFZgthfu5EGNwosaVB6BTkybXRBrxcmzeE2APJQQRrendi68VdPHM2giOFMN7tNG/0HMw3yXH8I9mTNedPlDl6nXc4Ou+KicQyL54BoH305UlSXUg0Jb5T0eLF+TxIO3IG/5jQau2J6tWVHyfdR8flH/PLq+8x7tnHKuw3+hmxSAvHLx2nV2CvCvvGdQ/Gd5WORdsTHeboq0M5+mamVE75WO/Haj5YobgC1y5diFi6lM+3HuZ4viC5AD7//TRr4y7SwUfPaGMgzk4ajl3IZdfpDPKLrdLMUD89Xq46dFoNPx+uPFGo1QiCvVwJ8HQhwNOFQE8XAj1dCfS6/D7UT4+PW/UFxxtCf3cvDublkJBl5r5Oozid/y1uOj1PdbuV/WlxfHk+HfBkfWYJf6/mPBbbF5jWVuR835o9bPkqGSnduWaghcNbS9j0/mZue3uK3faF6dk4+3qg0Wi4/oUnWL1rO6GLPiBu2LUYB1/OZV+aCiEuI66So3fVabm9bwifbTnNxZxCu9lCHYly9M1IaXZKVexb0RCEEAy9rjtDbZ9njO7MllNpLNyWyMq9KRSbLIQb3Li9bwgDIg30j/QjwLNiFais/BKcnTRYpOTw2WzWH7vIhaxCLuYUkZiex87TGWTmV86vHuDpQpcgTzoFedj+9aRzkAeejfBr4KZQA58czeGLhIvcEOHPv3rfVrbv5ePH2GuKZJw+gT9FdKz2PE7ObmhdB/DDmwcxlSRQUuSLRkiGTQmi+/CemE8s4nBGO06u3EzHiYPL2hVl5rL6ue9IKQnGvSSdGx7uQVC/rvT/4E1O3TqB9CefJPSnb/Hwsa4zCHILws/Vz26cHmBK/zA+2pTA8l3JPDqiepsbG+Xomwklp1Q4Cm83HeN7tGN8j9rVPS1tU0r/SD/6R1YeeBSZzKTlFnMx+/IXwPELuRy/kMPSHWcoKLm8kKu9tyudgz3pHGR9+bnryC4wMdIYWOuQ0LXtfPDaY2azpqBSxacxfu5sOQ95Zkn/wOqzqgjC0em7UZhnu1dDBhOfGoe7twcAg56ZyKk/r2bbT2aiJ0gK07PY9+HPHI53oUgXjL44gzxnAys/PM2fenciMKwdCc+8RLvnZrBx1jxufMX6e0IIgdHPaFd5AxAV4MGgaANLdiTx8LBotE04j6IcfTOx/Nhy4rPieXPEm0pOqWgVuDhp6eCjp4OPvtI+i0WSfKmA4xdyOHYhhxMXcjh2IZetp9IpNl1ejermrGVApB9ajQY/dx0v3NwNd5eq3dB4Xy+WFuWx4Nh57jdeVhM9bBxLWvF3vJMRxeuHfuCZnrdUeQ7/sCAgj04DzYR2ao/xuoo5gJw99MREFrMnJZD3pq9HWExITQDowNOSwU1/68/3c7eT42RgzbMruP71Oxjwh3Gsnvc+Hmu+x/yvmWhtuYCMBiPzD82n2FyMs7ZyWGvqgDAeW7yX306kMqJLzYVNGgvl6JuBrKIs3ttvlVOOCB3R3OYoFA1GoxGEGdwIM7gxOuZyLhmT2UJiRj7pucUUmyz8fPg8W0+lcSrVOrzuH2ng9r4hVZ73+d4RLNt0kDfiKzp6gGd63MTvW1exIN2HGaZi9E725wtc3d2BPPxCJMbrutk9ptdjN7HnaWvy3Y6GS3QZ1QUnjRnf7n1xC/Tm7g9D+PrxpZy65E/Ct5uJvHUwLhNvJ/DtV9ix4ieuvfNGwDoha5ImTmSeoJuh8rXGxgTj7+HM4u1JTeroW9/yuTbA+/vfJ6c4h7/FqmLfiraNk1ZDdIAH/SP9GNzJn5cndOfXmcMZYAsN3XxN9eElP72OYTpXUt00fBt/scI+jUbDPcEeZOHN5gtHqjyHVmOdjzCbql6Zqvf1oOdI6xdOu+uvI3x0LzqMtDr50mtd/9x4dOYCdv9sVfEMuv92slw8SFu8pOw8ZROyVcTpnZ00/CE2lF/jLnAuq6Dae29MlKNvYpScUqGAW3tZk9ieTsuv8dhZsVEIk4V/HU0B4EhGLkN/2kPUut95Jck6Qbw+9VzVJ7ANpiTVrxnqPtRq045V8ZWSnwF4BPvgp80kt8gaanV103Mxuhve5xLLjgnxCMFT51mloweY0i8MCXy580y19jQmytE3MXN2zbFmp+ylslMqrl5GGa1hi7VxF2o8NtTTlWs1zqToBWN/PsDoncc57ixwIR8vsugu9zHMr2rVWtkkbg1rQ32D3YnqHUBhnomtX52qtD/1QALpFj90wvrlknTkFCHH95LVPqLsGCFEpRqyVxJmcGNIpwCW7jiDyVz5C8URKEffhGxO2cymlE08fM3DGPSG5jZHoWg2grxciWnnxZaTtSuGvniYkQ4FkgPOVse4IDqMvYOvY7rlME/zMpdO/Vpl29LwaG2yAIx5IAatk+DQxorFQkz5RXz71gGQklH3xmCxWDg405qZ/ZpXX6xwrNHPyLGMY5RYKstRS7lrQBjnswtZf6xpUrIrR99ElFhKmL1ztpJTKhQ2jO28OHkxt+YDAVcnLbvH9+bn7tEcGNqd68MNuDq7MnX0v0gu6IePaQH74rfZbStsI/raOHonnZYOnX0xmyRn4i7nkC9Mu0SRkycd2xUQPOQa1r29gKiEg1yY/ABhMRUTlRkNRootxSRkJVR5nVFdAwnycmHx9sQqj2lMlKNvIkrllDNjZyo5pUIBRAW4czGniJzCqke+V3JNgCf++orqmnGD/keeyZOTx/5CZl7lAh+XR/TVn9tisnB4cwoFuVZ7tq48WbbPLSQQ7+LzHL3ox88zv8B58UJSDCGMebpyCLamCVmwTlLf3jeEjcdTOZ9VWL1hjYBy9E1AmZwyWMkpFYpSogOsRUKOnc+p4cjqCfINJihsFr4uaazZNb/CPpPZxPajqwE4tFfy1arjXLQzAZyXVcSCZ7eyYeExUpNy0Llq6Tb4ctVTjUbDrS+OItwjlZO57Yjr/hcuBffBSVdZoR7uGY7eSV9tnB5gcmwoFgkrdjt+Ulbp6JuAefvnWeWU/ZScUqEoJaadN0LAL0cuEBvRsBQgg7uNZMnpMCz8CPwVgNz8DFZtvBd/p+O4+DxNYUoo51OSWfTjGfqMjyCmVyDrPo/j0rk8zCbrcL9DFx/63hBBSBffSn+rnmFB3PjGVM5uO8qqj4+Q4j+CwiITrlcs+NJqtHTx7VLtiB4g3ODOwCg/lu1KZvrwjg7NOKpG9A4mPkvJKRUKe4QZ3Litdwc+2hTPvI2nGpyr3cnjRoL1Cazb9w1p2Rf4YcPt+OqOke78F6a+NJn75g4helIEzlJw6IdElv1nJ2lncjFbJEFRXgye3IkJM/oQ2tWv2gFZ+4FdCZrQA51wZu1G+7npS5U3Flm9quaOfqEkZeSzLSG9QfdeE8rRO5g5O+fg6uSq5JQKhR1eua0H43u047XVR3lp1eFaTZhWxU3XPkpqQQfyLjzHlt/H4qU7i9n7de4cNh03Fzc83J0ZNyYKi+8Vc2QWiE/MIsVc+7mCsHDrQqr8XPttjH5GCkwFJGZXP9l6Q/d2eLo6sczBmnrl6B1IqZzyTz3/pOSUCoUdXHVa3pnSm2mDI1nweyIzl+3nQnb9Jif1znq6GudQZNHjJEpwC/4fN/a7rdJxj786BONdHXG7LgDXAQa8hgXhaoa9P9kfndvDbNO/a7T298cYrInWagrfuOq03NqrPasPnSeroPZfNHVFxegdhMliYvbO2YR6hjLVqOSUCkVVCCF4ZrwRIWDB1kTWHLnAzLGdmTogvELhlNrQK6o/3cO3kluQg4+Hb5XHjRwSBkMuf/7PnlS0hRYys4vw8XKpsl0pZluYqaoQT5RPFDqNjriMOMZHVV899Y7YMBZuS+K7fSncc21EjdeuD2pE7yBKi30/Gfuk3Sx2CoXiMhqN4NkbY/hlxlB6hfnw4qojDHz1V5bvqntIw0nrVK2Tt0dwNz88SiTv/3MLpxIzazze1cU6lC8uMtvdr9Po6OzbucYRPUD3Dl4Y23nxZT3utbYoR+8AlJxSoagfEf7ufP5Afz67vx8dAzz424oDPLXiAIUl9h1qY/HgfT2JmhSJa4nkq1m7OXO2eslnu0CrNDQ7s6jKY0onZGuadxBCcEdsCIdSsjl8NqvuxtcC5egdgJJTKhT1RwjBiC6BLP6/ATw6Ipovd51h/FubWLX/rEOvO35MFP3v74qbGVZ+ebTaYwMMesxI8rKqzohp9DOSXZzN2bya7Z7QuwPOThq+3pNSZ7trg3L0jUypnHJip4lKTqlQNAAnrYa/Xd+Vz+7vh9kieXzJXlJzqh5BNwbX9e9Atp+OkhPZ5NopnViKRqOhQCsoyq7antpOyAJ463W4aDVlBdobG+XoG5lSOeVjvVSxb4WiMRjRJZCZY62DpiuLmDuCvqPD0FsEX393otrjzH46tOnFdlMaA3Ty7YRWaDmSXnWu/FIy80vIKTIRZnCvl801oRx9I6LklAqFY+gcZK3vui3esQuLAMYMDyPXCU7vuljtcUHR3ribBcdOZdrd76J1IdonusZUCACJGda0DGF+bnW2tzYoR99IKDmlQuE4ugZ70aODN59sTnD4xKxGo8Gzqze+uWYOnaycJK2Uvv2CAdixo+oYvNHPyJH0IzVOyCYpR986WH78cnZKJadUKBqfp8d3JSkjn+e+OdSgFbS1Ydz4aASC71ccq/KYnkZ/CjSSc8czqzzGaDCSUZhBakH1eeeT0q01dB3l6NWCqUYgqyiLd/e9S//g/owMHVlzA4VCUWcGRfvz51GdeOvXE/i6O/P0DV0dpmqLivIhv50LnqcLmPWfrcQODaGk2IybuzO9ewTg4e6MRqOh2OCMU1oRFovlciWrcpSfkA10q7oYeFJGPgGeLuidq1hq20DUiL4RKJVT/r3f35WcUqFwIDNGd+KegeF8+Fs8645WH0NvKE88ey1F0e64nykkbtFJTi5P4MD8Y7zz1GZ27T8P1Byn7+LbBYHgSEb1E7KJ6fmEO2g0D8rRNxglp1Qomg4hBE+M7gRYnaMj0Tlp+OvfBtD7wa5E3h5JpzujMYxuj9Yi+eVTq+Pu07f6OL2bzo0I74gaJZZnMvIdFrYBFbppMHN3zcXFyUXJKRWKJqI0m7FO2zS/ngf1a1/h89z4TNzi87BYLBw4YP1V4e5R9byc0c/Inot7qtxfZDJzLruQMIMa0bdItqRs4bfk35ScUqFoQkw23bqTtnncl3eAHh2CDz/eT+qWC2S6CSbe3LHK42MMMZzPO09GoX0FT/KlAqR03EQsKEdfb0wWE7N2ziLUM5S7jHc1tzkKxVVDaebItUcukHzJseEbe0ya2IUcHZj3XMLJAiOndrU7EVuK0c9aQ/Zouv20CqXSynAHjuhV6KaelMop/zfif0pOqVA0Ie289TxwXSQLtyUyYs4GJseGcv91kXQM9GiS6/t6u/LY60M4k5yDwdeVwIDqHXRXQ1cAjmQcYVCHQZX2J9nmGkJVjL5loeSUCkXzodUInr85hmlDInl73UmW70pm0fYkogPcGdY5EK0Ggrxcub1vCD5ujhmEebjpMHauXZ1bL2cvQjxCqpyQTcrIR6/TEuBRcx78+lKjoxdCuAK/AS6241dIKV8QQvQC5gGugAmYLqXcYaf9OOBNQAt8LKV8rfHMbx6UnFKhaH7a++h5dWIP/jqmM6v2n+XXoxdYtD2RIpM1hv/+hlN8Pf06h05y1pbSlMX2SEy3Km4c6UtqM6IvAkZKKXOFEDpgsxBiNfAv4CUp5WohxHhgFjC8fEMhhBZ4FxgDJAM7hRDfSSlrzvLTQlFySoWiZRHg6cIDgyN5YHAkJrMFjRDsTrrEg/N3Mn3xblY8PAhXnWMWItWWGEMMaxLXkF2cjZezV4V9ZzLyHRq2gVpMxkorubaPOttL2l6lFnsD9oSk/YGTUsp4KWUxsBS4tcFWNyNKTqlQtFyctBo0GkG/CD/emNyLQynZ/Ov75h9XdvWzxumPZVRMqSClJCkj36ETsVBL1Y0QQiuE2AdcBNZIKbcDTwCzhRBngDnA03aadgDK18dKtm1rlSg5pULRehgdE8TDw6JZvD2JlXuSm9WWUkd/Zcri1NwiCkrMDpVWQi0dvZTSLKXsBYQA/YUQ3YFHgBlSylBgBvCJnab2gk52sxEJIR4SQuwSQuxKTa0+AVBzUJqdMsQjRMkpFYpWwpNjO9M/0o9nvz7EsfPVlwd0JP56fwLdAivF6RNSrcnMIv0dk4e+lDrp6KWUmcAGYBxwL7DStms51jDNlSQDoeU+h2A/xIOU8kMpZayUMjYgIKAuZjUJy48v51TWKVXsW6FoRThpNbwzpTfuLk788dPtnErNrbmRg4jxi6mkvElIayGOXggRIITwsb3XA6OBo1gd9jDbYSMBe+VYdgKdhBCRQghn4E7gu0awu0mpIKcMU3JKhaI1EejlysJp/SkxS/6ydK/DyvXVhNFgJCErgfySy4u84tPycHbS0N5H79Br12ZE3w5YL4Q4gNVxr5FSfg/8HzBXCLEfeAV4CEAI0V4I8SOAlNIEPAb8DMQBy6SUhxv/NhzLvP3zyC7KVnJKhaKV0jXYi1du686hlGzu+2wHabmOrT1rD6OfEYnk+KXjZdviU/OIMLih1TjWr9Qor5RSHgB629m+GehrZ/tZYHy5zz8CPzbMzOYjIStBySkVijbAuO7tmHV7T5775hAT39vKikeuJdDTtcmubzRYUyEcST9Cr8BeACSk5dIp0NPh11a5bmpgzq45VjllbyWnVChaO5NjQ1ny0EDScov44yc7yMovabJrB7kF4efqVzYhazJbSMrIJzLAsfF5UI6+WsrLKf31/s1tjkKhaAT6hPny4T2xxKfm8YcPtvLtvhSKTY6P2wshMPoZyyZkUzILKDFLh0/EgnL0VaLklApF22VwJ38+uKevbYJ2H4NfX8dbv57gXFaBQ69rNBg5lXmKYnMx8TZpZVQTOHqV1KwKVhxfwamsU/xvuMpOqVC0RUZ0DWRY5wA2nkhl/pbTvLHmOG+sOU639l6M7BrItVEG+oT7Nmr6BKOfEZM0cSLzBPFp1kVSTTGiV47eDqVyyn7B/ZScUqFow2g0ghFdAhnRJZCEtDx+OnSetXEXeHf9Sd5edxJnJw2jugYyqU8Iw7oEoGtgsZPSCdm49DgS0rrgrdfh5+74gaRy9HaYt38eWUVZPNXvKSWnVCiuEiL93XlkeDSPDI8mp7CEnacz+O14Gqv2n2X1ofP4e7jw0NBI7h4Yjptz/VxniEcInjpPm6MPIdLfvUl8jIrRX4GSUyoUCk9XHSO7BvHiLd3Y9swoPvpjLF2DPXnlx6MMnbWer/cmI6XdbC7VIoQoS1kcn5rXJPF5UI6+EmXZKZWcUqFQADqthjExQSycNoAVD19LiK8bM77czx8/3VFWHaouGP2MHMs4xrmsvCaJz4Ny9BXYmrKVjckbeajnQ0pOqVAoKhEb4cdXjwzipVu6sSfxEmP/t5H3NpysU1oFo8FIsaUYjUtqk2joQTn6MkwWE7N3WeWUdxvvbm5zFApFC0WrEdw7KIK1M4cxrHMAs346xo1vbWLn6YxatS+dkNW4phDl3zR1bpWjt7Hi+ApOZp5U2SkVCkWtaOet54N7Yvn4j7HkFZn5w7zf+euX+7iYXVhtu3DPcJyEK1rXFCL8m6bMoXL0KDmlQqGoP6Njgljz16E8NqIj3x84x8i5G/l4U3yV4RytRosHYbi6n6+3eqeuKEcPfHDgA7KKslR2SoVCUS/cnJ148vou/DJjKP0j/fj3D3Hc8OYmNp9Is3u8LOoAzilYZNOkTL7qHX1CVgJL4pYwsdPEsnJfCoVCUR8i/N359L5+fHJvLCVmC3d/sp1HF+0hp/By8jQpJVlZgVhEEYnZiU1i11Xv6JWcUqFQNDajjEH8/MRQZo7pzM+Hz3P3x9spLDEDcCm/hLycYIBKFaccxVXt6JWcUqFQOApXnZbHR3Xinal92J+cxYvfWWsuJaTlYikKxEnoKtWQdRRXbQoEJadUKBRNwbjuwTw6Ipp315+iS7An7i5OgJZIr05NNqK/ah19qZzyv8P/q+SUCoXCofx1TBfizuXw0qojZdt6BsbwS+LPSCkdLgK5KkM35eWUo8JGNbc5CoWijaPVCD64py+TY0PKtsUYjOQU55CSm+Lw61+VI3olp1QoFE2NTqth1u3XMNoYhLdeh7vXOQDiMuII8QypoXXDuOoc/ems00pOqVAomo2x3ayKmyKzB1qhJS49jjHhYxx6zasudKOKfSsUipaAi9aFaJ9ojmQcqfngBnJVOfpSOeX/9fg/JadUKBTNTmmx8Prktq8LV42jLy+nvCfmnuY2R6FQKDAajGQUZnAx/6JDr3PVOPqvjn/FycyTzIydqeSUCoWiRRBjiAFw+MKpq8LRZxdn886+d4gNilVySoVC0WLo4tsFgXD4wqmrwtGXFvtWckqFQtGScNO5EeEd4fAJ2Tbv6MvLKUsruygUCkVLoXRC1pG0eUevslMqFIqWTIwhhgv5F8gorF0pwvrQph391rNb2ZC8QckpFQpFi8XoZ400HE0/6rBrtFlHb7KYmL1zNh08OnB3jMpOqVAoWiZdDdYV+o6M07dZR18qp3wy9klctC7NbY5CoVDYxcvZixCPEIfG6duko1dySoVC0ZowGowO1dK3SUf/wX6VnVKhULQeYgwxnMk5Q3ZxtkPO3+Yc/ems0yyOW8xtnW5TckqFQtEqKJ2QPZZxzCHnr9HRCyFchRA7hBD7hRCHhRAv2bZ/KYTYZ3udFkLsq6L9DFu7Q0KIJUII10a+hwqUyikf7/24Iy+jUCgUjUZpyvQj6Y6ZkK1NPvoiYKSUMlcIoQM2CyFWSynvKD1ACDEXyLqyoRCiA/BnIEZKWSCEWAbcCcxvFOuvoFRO+USfJ5ScUqFQtBoMegNBbkEOi9PX6OilNX9mru2jzvYqy6kprEHwycDIaq6hF0KUAG7A2YYYXB3v7nsXgJUnVrLq1CpHXUahUCganQv5F/gh/gdeG/Jao5+7VhWmhBBaYDfQEXhXSrm93O4hwAUp5Ykr20kpU4QQc4AkoAD4RUr5SxXXeAh4CCAsLKxON1HKmLAxBLkF1autQqFQNCensk4BOKRYuKhLwnshhA/wNfC4lPKQbdv7wEkp5Vw7x/sCXwF3AJnAcmCFlHJhddeJjY2Vu3btqrVdCoVCcbUjhNgtpYy1t69OqhspZSawARhnO7ETMBH4soomo4EEKWWqlLIEWAkMqss1FQqFQtEwaqO6CbCN5BFC6LE679KkDKOBo1LK5CqaJwEDhRButlj+KMCxadoUCoVCUYHajOjbAeuFEAeAncAaKeX3tn13AkvKHyyEaC+E+BHAFstfAewBDtqu92Ej2a5QKBSKWlCnGH1ToWL0CoVCUTcaLUavUCgUitaHcvQKhULRxlGOXqFQKNo4ytErFApFG6dFTsYKIVKBxHo29wfSGtEcR6HsbHxai63KzsaltdgJjrU1XEoZYG9Hi3T0DUEIsauqmeeWhLKz8Wkttio7G5fWYic0n60qdKNQKBRtHOXoFQqFoo3TFh19a1l5q+xsfFqLrcrOxqW12AnNZGubi9ErFAqFoiJtcUSvUCgUinIoR69QKBRtnFbh6KsqRC6E0AkhFgghDgoh4oQQT1fR3k8IsUYIccL2r2+5fU8LIU4KIY4JIa53oK13ldu+TwhhEUL0qkP7CCFEQbl985rZzheFECnljhtfbl+j9Wkj2DlbCHFUCHFACPF1uZTbLa0/m/0Zte3rKYT4XQhx2PZ35VqHe22SPq2Dnc36jNbBTsc/o1LKVvUC5gLP295PBZba3rsBp4EIO21mAf+wvf8H8LrtfQywH3ABIoFTgNYRtl6xvQcQX8d7jQAOObpPa2sn8CLwpJ3tDuvTeto5FnCyvX+93P99S+vPZn9GsZYWPQBcY/tsqOlazfGM1tbO5n5G62Cnw5/RVjGiL0WIskLkpTnwJeAurJWu9EAxkG2n6a3AAtv7BcCEctuXSimLpJQJwEmgv4NsLc+UKrbXtn2j0VA77eCQPq2vnVLKX6SUJtvHbUBIQ22pjgb0Z0t4RscCB6SU+wGklOlSSnMd2juEhtpph6Z6RmtlZ1M8o63K0VO5EPkKIA84h7Wa1RwpZYaddkFSynMAtn8Dbds7AGfKHZds2+YIW8tzBzX/cdhrHymE2CuE2CiEGNIC7HzM9nPz03KhBkf1aUP7E+ABYHW5zy2pP1vCM9oZkEKIn4UQe4QQf69je2iaPq2Lnc35jNa1P8FBz6hTfRs2NkKItUCwnV3PSim/tb2/ckTUHzAD7QFfYJMQYq2UMr62l7WzrUa9aT1tLW07AMiXtuLq1XBl+3NAmJQyXQjRF/hGCNFNSmnvF0xT2Pk+8DLW/noZ60/WB6hHnzZFfwohngVMwCLbppbWn1Ve1s42Rz2jTsBgoB+QD/wqrMUsfq3iMs31jNbWzuZ+RuvUn43xjFZFi3H0UsrR1e0XlwuR9y23eSrwk7QWHr8ohNgCxAJXOvoLQoh2UspzQoh2wEXb9mQgtNxxIcBZB9laSqXyi7VpL6UsAops73cLIU5hHTFUWYrLkXZKKS+UO89HQGl5yTr3aRP0573ATcAoaQt+trT+pGU8o8nARillmu2YH4E+QCXH1MzPaK3sbAHPaF36s1Ge0epuoFW8gHG2Tiu/7SngM6zf0O7AEaCnnbazqTjRNcv2vhsVJ2XiaYRJGXu22rZrbP/5UfW414BS24AoIAXway47gXbl3s/g8qR4o/dpA+0cZ3suAlp4fzb7M4r1V/EerMIGJ2AtcGNLe0Zra2dzP6N1sNPhz2iDHpamfAHzgYev2OYBLAcO2zrqb+X2fQzE2t4bsH6LnrD961fuuGexzrofA25wlK227cOBbXa2l9lazb1Ost3nftvDc3Nz2gl8gbXg+wHguyv+qBq1Txto50ms8dh9tte8FtqfLeUZvdvWL4ewfdm00Ge0RjtbyDNaGzsd/oyqFAgKhULRxmltqhuFQqFQ1BHl6BUKhaKNoxy9QqFQtHGUo1coFIo2jnL0CoVC0cZRjl6hUCjaOMrRKxQKRRvn/wGVU3f7DzTY7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  263227.0 people (VAP of 201461.0 ) and  133760.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 50\n",
      "working on tract 100\n",
      "working on tract 150\n",
      "working on tract 200\n",
      "working on tract 250\n",
      "working on tract 300\n",
      "working on tract 350\n",
      "working on tract 400\n",
      "working on tract 450\n",
      "working on tract 500\n",
      "working on tract 550\n",
      "working on tract 600\n",
      "working on tract 650\n",
      "working on tract 700\n",
      "working on tract 750\n",
      "working on tract 800\n",
      "working on tract 850\n",
      "working on tract 900\n",
      "working on tract 950\n",
      "working on tract 1000\n",
      "working on tract 1050\n",
      "working on tract 1100\n",
      "working on tract 1150\n",
      "working on tract 1200\n",
      "working on tract 1250\n",
      "working on tract 1300\n",
      "working on tract 1350\n",
      "working on tract 1400\n",
      "working on tract 1450\n",
      "working on tract 1500\n",
      "working on tract 1550\n",
      "working on tract 1600\n",
      "working on tract 1650\n",
      "working on tract 1700\n",
      "working on tract 1750\n",
      "working on tract 1800\n",
      "working on tract 1850\n",
      "working on tract 1900\n",
      "working on tract 1950\n",
      "working on tract 2000\n",
      "working on tract 2050\n",
      "working on tract 2100\n",
      "working on tract 2150\n",
      "working on tract 2200\n",
      "working on tract 2250\n",
      "working on tract 2300\n",
      "working on tract 2350\n",
      "working on tract 2400\n",
      "working on tract 2450\n",
      "working on tract 2500\n",
      "working on tract 2550\n",
      "working on tract 2600\n",
      "working on tract 2650\n",
      "working on tract 2700\n",
      "working on tract 2750\n",
      "working on tract 2800\n",
      "working on tract 2850\n",
      "working on tract 2900\n",
      "working on tract 2950\n",
      "working on tract 3000\n",
      "working on tract 3050\n",
      "working on tract 3100\n",
      "working on tract 3150\n",
      "working on tract 3200\n",
      "working on tract 3250\n",
      "working on tract 3300\n",
      "working on tract 3350\n",
      "working on tract 3400\n",
      "working on tract 3450\n",
      "working on tract 3500\n",
      "working on tract 3550\n",
      "working on tract 3600\n",
      "working on tract 3650\n",
      "working on tract 3700\n",
      "working on tract 3750\n",
      "working on tract 3800\n",
      "working on tract 3850\n",
      "working on tract 3900\n",
      "working on tract 3950\n",
      "working on tract 4000\n",
      "working on tract 4050\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "if type(tractMAP) != type(tractGeom[1]):\n",
    "    for geom in tractMAP.geoms:\n",
    "        x,y = geom.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "else:\n",
    "    x,y = tractMAP.exterior.xy\n",
    "    plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a924cee7-09a5-4b00-b6d2-509c833975f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 3.0036298193425064 1.7651071916949967\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"original and final map areas are\",wholeMAP.area,tractMAP.area)  #same if we didn't buffer\n",
    "x2, y2 = tractMAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x2, y2 = wholeMAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.2857142857142857 1407\n",
      "100 0.2897822445561139 1791\n",
      "200 0.1762928139691068 2978\n",
      "300 0.3524537922243467 6276\n",
      "400 0.18057553956834532 1390\n",
      "500 0.12153344208809136 1226\n",
      "600 0.0 0\n",
      "700 0.0 0\n",
      "800 0.606318347509113 823\n",
      "900 0.0 0\n",
      "1000 0.48565573770491804 488\n",
      "1100 0.5478527607361963 1630\n",
      "1200 0.04460093896713615 852\n",
      "1300 0.4355885078776645 1079\n",
      "1400 0.02577319587628866 776\n",
      "1500 0.171334431630972 1821\n",
      "1600 0.29851315287838354 2623\n",
      "1700 0.4703489281802535 6391\n",
      "1800 0.31216216216216214 1480\n",
      "1900 0.5889199255121043 2148\n",
      "2000 0.20544090056285177 1066\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and buffered map areas are 1.7651071916949967 1.9247053496385527\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = tractMAP    # let's keep a copy of the original map prior to buffering\n",
    "MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and buffered map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x2, y2 = origMAP.exterior.xy\n",
    "#plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c159b41-0ef3-44b4-9845-d98fb87e6d4d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  6177182 2961409 0.3297095295290766\n",
      "compare to Census 6177224 Leip has Trump + Biden = 2961437.0 0.3297095295290766\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 6177224\n",
    "atlasTrump = 976414.\n",
    "atlasBiden = 1985023.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 6177182 6177182.0\n",
      "state Trumpers before, after slice opn are 976399 976399.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b5334311-b597-4568-88c7-eb3c9d4bdf09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 84 grids for 8 districts\n",
      "starting grid no 0 at x = -77.71603354166666, y = 38.64135264285714 \n",
      "starting grid no 5 at x = -77.71603354166666, y = 39.47277907142857 \n",
      "starting grid no 10 at x = -77.54156262500001, y = 39.14020849999999 \n",
      "starting grid no 15 at x = -77.36709170833332, y = 38.807637928571424 \n",
      "starting grid no 20 at x = -77.36709170833332, y = 39.63906435714286 \n",
      "starting grid no 25 at x = -77.19262079166666, y = 39.30649378571429 \n",
      "starting grid no 30 at x = -77.01814987500002, y = 38.9739232142857 \n",
      "starting grid no 35 at x = -76.84367895833333, y = 38.641352642857136 \n",
      "starting grid no 40 at x = -76.84367895833333, y = 39.47277907142857 \n",
      "starting grid no 45 at x = -76.66920804166665, y = 39.14020849999999 \n",
      "starting grid no 50 at x = -76.494737125, y = 38.807637928571424 \n",
      "starting grid no 55 at x = -76.494737125, y = 39.63906435714285 \n",
      "starting grid no 60 at x = -76.32026620833332, y = 39.30649378571429 \n",
      "starting grid no 65 at x = -76.14579529166667, y = 38.9739232142857 \n",
      "starting grid no 70 at x = -75.97132437500001, y = 38.641352642857136 \n",
      "starting grid no 75 at x = -75.97132437500001, y = 39.47277907142857 \n",
      "starting grid no 80 at x = -75.7968534583333, y = 39.14020849999999 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 2877277.92475586 21800883.15069422 74.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 8   #MD congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 3  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9550934903092565e-08 2.699885000008878e-06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 11 3.0 3 47.9 0.4798 210422.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 12 3.0 3 90.0 0.6487 188340.2\n",
      "I am working on tract number 20 of 4079 tracts\n",
      "I am working on tract number 40 of 4079 tracts\n",
      "I am working on tract number 60 of 4079 tracts\n",
      "I am working on tract number 80 of 4079 tracts\n",
      "I am working on tract number 100 of 4079 tracts\n",
      "I am working on tract number 120 of 4079 tracts\n",
      "I am working on tract number 140 of 4079 tracts\n",
      "I am working on tract number 160 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 167 7.0 0 94.9 0.8333 180913.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 168 8.0 0 92.7 0.8396 191177.1\n",
      "we have 2 non-opposing shorted wedges for tract no 172\n",
      "I am working on tract number 180 of 4079 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 188 1 199106.77084778438 0.4936\n",
      "8 yoyos for tract,wedge,wedgePop,r= 188 1 160918.9264672848 0.2468\n",
      "9 yoyos for tract,wedge,wedgePop,r= 188 1 199269.33027541282 0.5013\n",
      "10 yoyos for tract,wedge,wedgePop,r= 188 1 167895.6720996967 0.3741\n",
      "10 yoyos for tract,wedge,wedgePop,r= 188 1 187721.91600432512 0.4377\n",
      "11 yoyos for tract,wedge,wedgePop,r= 188 1 198273.4007559783 0.4695\n",
      "I am working on tract number 200 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 216 6.0 0 87.8 0.6073 341440.0\n",
      "I am working on tract number 220 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 227 5.0 2 90.0 0.8059 197452.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 227 6.0 2 90.0 0.7923 197452.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 227 7.0 2 90.0 0.7789 197452.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 227 8.0 2 90.0 0.7658 197452.8\n",
      "I am working on tract number 240 of 4079 tracts\n",
      "I am working on tract number 260 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 266 6.0 3 49.1 0.7508 251239.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 272 2.0 3 90.0 0.1817 26157.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 275 6.0 3 41.3 0.8979 211073.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 275 7.0 3 41.3 0.8391 211073.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 275 8.0 3 41.3 0.7841 211073.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 275 9.0 3 41.3 0.7327 211073.7\n",
      "I am working on tract number 280 of 4079 tracts\n",
      "I am working on tract number 300 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 304 5.0 0 93.0 0.7691 195203.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 304 6.0 0 93.0 0.7627 195203.5\n",
      "I am working on tract number 320 of 4079 tracts\n",
      "I am working on tract number 340 of 4079 tracts\n",
      "I am working on tract number 360 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 362 2.0 0 90.0 0.3297 182615.7\n",
      "I am working on tract number 380 of 4079 tracts\n",
      "I am working on tract number 400 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 407 8.0 0 92.6 0.639 183421.6\n",
      "I am working on tract number 420 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 4.0 3 38.5 0.8605 197572.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 5.0 3 38.5 0.8456 197572.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 6.0 3 38.5 0.831 197572.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 7.0 3 38.5 0.8166 197572.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 8.0 3 38.5 0.8025 197572.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 9.0 3 38.5 0.7886 197572.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 10.0 3 38.5 0.7749 197572.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 11.0 3 38.5 0.7615 197572.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 421 12.0 3 38.5 0.7483 197572.7\n",
      "I am working on tract number 440 of 4079 tracts\n",
      "I am working on tract number 460 of 4079 tracts\n",
      "I am working on tract number 480 of 4079 tracts\n",
      "I am working on tract number 500 of 4079 tracts\n",
      "I am working on tract number 520 of 4079 tracts\n",
      "I am working on tract number 540 of 4079 tracts\n",
      "I am working on tract number 560 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 566\n",
      "we have 2 non-opposing shorted wedges for tract no 567\n",
      "we have 2 non-opposing shorted wedges for tract no 569\n",
      "we have 2 non-opposing shorted wedges for tract no 576\n",
      "we have 2 non-opposing shorted wedges for tract no 579\n",
      "I am working on tract number 580 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 581\n",
      "we have 2 non-opposing shorted wedges for tract no 585\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 7.0 2 94.0 1.6087 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 8.0 2 94.0 1.5379 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 9.0 2 94.0 1.4702 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 10.0 2 94.0 1.4054 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 11.0 2 94.0 1.3435 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 16.0 2 94.0 1.5614 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 17.0 2 94.0 1.4927 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 18.0 2 94.0 1.4269 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 19.0 2 94.0 1.3641 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 24.0 2 94.0 1.8997 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 25.0 2 94.0 1.8161 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 26.0 2 94.0 1.7361 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 27.0 2 94.0 1.6597 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 28.0 2 94.0 1.5866 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 29.0 2 94.0 1.5167 379057.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 30.0 2 94.0 1.4499 379057.1\n",
      "loop31.0, tr585,wedgePops794262.57, 36222.1, 21668.5, 379057.1, 357314.9, Overedge?1, 1, 0, 0, ,Satisfied?0001,yoyo?0051 \n",
      "   targetWP, latest drx4 are tWP,dr, 357128.6,0.1175, 357128.6,0.1478, 357128.6,-0.0638, 357128.6,-0.0015\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 31.0 2 94.0 1.3861 379057.1\n",
      "loop32.0, tr585,wedgePops794262.57, 36222.1, 21668.5, 379057.1, 357314.9, Overedge?1, 1, 0, 0, ,Satisfied?0001,yoyo?0051 \n",
      "   targetWP, latest drx4 are tWP,dr, 357128.6,0.1175, 357128.6,0.1478, 357128.6,-0.061, 357128.6,-0.0015\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 585 32.0 2 94.0 1.3251 379057.1\n",
      "loop33.0, tr585,wedgePops786309.34, 36222.1, 21668.5, 371103.9, 357314.9, Overedge?1, 1, 0, 0, ,Satisfied?0001,yoyo?0051 \n",
      "   targetWP, latest drx4 are tWP,dr, 357128.6,0.1175, 357128.6,0.1478, 357128.6,-0.0583, 357128.6,-0.0015\n",
      "loop34.0, tr585,wedgePops658942.61, 36222.1, 21668.5, 243737.1, 357314.9, Overedge?1, 1, 0, 0, ,Satisfied?0001,yoyo?0051 \n",
      "   targetWP, latest drx4 are tWP,dr, 357128.6,0.1175, 357128.6,0.1478, 357128.6,-0.0877, 357128.6,-0.0015\n",
      "loop35.0, tr585,wedgePops740034.39, 36222.1, 21668.5, 324828.9, 357314.9, Overedge?1, 1, 0, 0, ,Satisfied?0001,yoyo?0061 \n",
      "   targetWP, latest drx4 are tWP,dr, 357128.6,0.1175, 357128.6,0.1478, 357128.6,0.0627, 357128.6,-0.0015\n",
      "loop36.0, tr585,wedgePops779763.88, 36222.1, 21668.5, 364558.4, 357314.9, Overedge?1, 1, 0, 0, ,Satisfied?0001,yoyo?0061 \n",
      "   targetWP, latest drx4 are tWP,dr, 357128.6,0.1175, 357128.6,0.1478, 357128.6,0.0193, 357128.6,-0.0015\n",
      "7 yoyos for tract,wedge,wedgePop,r= 585 2 364558.40668738727 1.261\n",
      "loop37.0, tr585,wedgePops592562.42, 36222.1, 21668.5, 177357.0, 357314.9, Overedge?1, 1, 0, 0, ,Satisfied?0001,yoyo?0071 \n",
      "   targetWP, latest drx4 are tWP,dr, 357128.6,0.1175, 357128.6,0.1478, 357128.6,-0.6305, 357128.6,-0.0015\n",
      "8 yoyos for tract,wedge,wedgePop,r= 585 2 177356.95242283802 0.6305\n",
      "loop38.0, tr585,wedgePops794262.57, 36222.1, 21668.5, 379057.1, 357314.9, Overedge?1, 1, 0, 0, ,Satisfied?0001,yoyo?0081 \n",
      "   targetWP, latest drx4 are tWP,dr, 357128.6,0.1175, 357128.6,0.1478, 357128.6,0.9861, 357128.6,-0.0015\n",
      "9 yoyos for tract,wedge,wedgePop,r= 585 2 379057.1011238777 1.6166\n",
      "loop39.0, tr585,wedgePops626076.72, 36222.1, 21668.5, 210871.2, 357314.9, Overedge?1, 1, 1, 0, ,Satisfied?0001,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 503385.9,0.1175, 503385.9,0.1478, 503385.9,-0.493, 503385.9,-0.0015\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.20924 28520.7111 36222.1097 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.23952 21248.1688 21668.5064 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.12353 379057.1011 210871.2446 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.63684 360316.5017 357314.8547 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr585,wedgePops731074.41, 36222.1, 21668.5, 210871.2, 462312.6, Overedge?1, 1, 1, 0, ,Satisfied?0000,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 503385.9,0.1175, 503385.9,0.1478, 503385.9,-0.493, 503385.9,0.0565\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.20924 28520.7111 36222.1097 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.23952 21248.1688 21668.5064 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.12353 379057.1011 210871.2446 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.69338 357314.8547 462312.5541 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 585, giving up w pop 731074.414731882\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 593 4.0 1 36.3 0.9701 248704.2\n",
      "I am working on tract number 600 of 4079 tracts\n",
      "I am working on tract number 620 of 4079 tracts\n",
      "I am working on tract number 640 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 656 8.0 3 75.0 0.5003 270000.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 657 7.0 3 72.7 0.4491 249271.0\n",
      "I am working on tract number 660 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 664\n",
      "we have 2 OPPOSING shorted wedges for tract no 672\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 679 6.0 3 60.3 0.9575 297722.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 679 7.0 3 60.3 0.8796 297722.1\n",
      "I am working on tract number 680 of 4079 tracts\n",
      "I am working on tract number 700 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 703 5.0 0 99.6 0.3643 174742.3\n",
      "I am working on tract number 720 of 4079 tracts\n",
      "I am working on tract number 740 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 751\n",
      "I am working on tract number 760 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 767\n",
      "we have 2 non-opposing shorted wedges for tract no 777\n",
      "I am working on tract number 780 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 784 4.0 2 90.0 0.6376 198550.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 784 5.0 2 90.0 0.6242 198550.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 784 6.0 2 90.0 0.6111 198550.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 784 7.0 2 90.0 0.5983 198550.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 784 8.0 2 90.0 0.5857 198550.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 784 9.0 2 90.0 0.5734 198550.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 784 10.0 2 90.0 0.5614 198550.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 784 11.0 2 90.0 0.5496 198550.9\n",
      "I am working on tract number 800 of 4079 tracts\n",
      "I am working on tract number 820 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 826\n",
      "I am working on tract number 840 of 4079 tracts\n",
      "I am working on tract number 860 of 4079 tracts\n",
      "I am working on tract number 880 of 4079 tracts\n",
      "I am working on tract number 900 of 4079 tracts\n",
      "I am working on tract number 920 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 923\n",
      "we have 2 non-opposing shorted wedges for tract no 926\n",
      "I am working on tract number 940 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 941\n",
      "we have 2 non-opposing shorted wedges for tract no 952\n",
      "we have 2 non-opposing shorted wedges for tract no 956\n",
      "I am working on tract number 960 of 4079 tracts\n",
      "I am working on tract number 980 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 990\n",
      "we have 2 non-opposing shorted wedges for tract no 992\n",
      "we have 2 non-opposing shorted wedges for tract no 993\n",
      "I am working on tract number 1000 of 4079 tracts\n",
      "I am working on tract number 1020 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1027 2.0 2 90.0 0.3343 22378.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1028 6.0 0 84.1 0.6753 274077.3\n",
      "I am working on tract number 1040 of 4079 tracts\n",
      "I am working on tract number 1060 of 4079 tracts\n",
      "I am working on tract number 1080 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1092 3.0 2 90.0 0.4635 224825.9\n",
      "I am working on tract number 1100 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1110\n",
      "I am working on tract number 1120 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1125 5.0 1 90.0 0.7333 231349.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1125 6.0 1 90.0 0.6381 231349.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1125 1 230897.583714655 0.5957\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1125 1 86934.48390979756 0.2978\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1125 1 230895.11933524098 0.5956\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1125 1 126376.30761673329 0.4467\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1125 1 201890.28789936565 0.5211\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1125 1 150792.69373106176 0.4839\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1125 1 175256.99590515104 0.5025\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1125 1 189485.90450723172 0.5118\n",
      "I am working on tract number 1140 of 4079 tracts\n",
      "I am working on tract number 1160 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1177\n",
      "we have 2 non-opposing shorted wedges for tract no 1178\n",
      "we have 2 non-opposing shorted wedges for tract no 1179\n",
      "I am working on tract number 1180 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1181\n",
      "we have 2 non-opposing shorted wedges for tract no 1185\n",
      "we have 2 non-opposing shorted wedges for tract no 1188\n",
      "we have 2 non-opposing shorted wedges for tract no 1190\n",
      "I am working on tract number 1200 of 4079 tracts\n",
      "I am working on tract number 1220 of 4079 tracts\n",
      "I am working on tract number 1240 of 4079 tracts\n",
      "I am working on tract number 1260 of 4079 tracts\n",
      "I am working on tract number 1280 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1295 4.0 2 378.2 0.604 232820.0\n",
      "I am working on tract number 1300 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1308 9.0 0 100.1 0.814 287879.4\n",
      "I am working on tract number 1320 of 4079 tracts\n",
      "I am working on tract number 1340 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1353\n",
      "we have 2 non-opposing shorted wedges for tract no 1355\n",
      "I am working on tract number 1360 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1362\n",
      "we have 2 non-opposing shorted wedges for tract no 1363\n",
      "I am working on tract number 1380 of 4079 tracts\n",
      "I am working on tract number 1400 of 4079 tracts\n",
      "I am working on tract number 1420 of 4079 tracts\n",
      "I am working on tract number 1440 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1446\n",
      "I am working on tract number 1460 of 4079 tracts\n",
      "I am working on tract number 1480 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1495\n",
      "I am working on tract number 1500 of 4079 tracts\n",
      "I am working on tract number 1520 of 4079 tracts\n",
      "I am working on tract number 1540 of 4079 tracts\n",
      "I am working on tract number 1560 of 4079 tracts\n",
      "I am working on tract number 1580 of 4079 tracts\n",
      "I am working on tract number 1600 of 4079 tracts\n",
      "I am working on tract number 1620 of 4079 tracts\n",
      "I am working on tract number 1640 of 4079 tracts\n",
      "I am working on tract number 1660 of 4079 tracts\n",
      "I am working on tract number 1680 of 4079 tracts\n",
      "I am working on tract number 1700 of 4079 tracts\n",
      "I am working on tract number 1720 of 4079 tracts\n",
      "I am working on tract number 1740 of 4079 tracts\n",
      "I am working on tract number 1760 of 4079 tracts\n",
      "I am working on tract number 1780 of 4079 tracts\n",
      "I am working on tract number 1800 of 4079 tracts\n",
      "I am working on tract number 1820 of 4079 tracts\n",
      "I am working on tract number 1840 of 4079 tracts\n",
      "I am working on tract number 1860 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1860 2.0 2 90.0 0.1841 48593.2\n",
      "we have 2 non-opposing shorted wedges for tract no 1871\n",
      "I am working on tract number 1880 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1893 9.0 0 95.4 0.6811 210606.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1893 10.0 0 95.4 0.6376 210606.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1893 11.0 0 95.4 0.5968 210606.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1893 12.0 0 95.4 0.5586 210606.1\n",
      "I am working on tract number 1900 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1906 9.0 0 98.2 0.8003 283493.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1909 6.0 3 43.5 0.8299 242754.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1909 3 242754.55337146646 0.7553\n",
      "I am working on tract number 1920 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1924\n",
      "we have 2 non-opposing shorted wedges for tract no 1935\n",
      "I am working on tract number 1940 of 4079 tracts\n",
      "I am working on tract number 1960 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1962\n",
      "we have 2 non-opposing shorted wedges for tract no 1963\n",
      "I am working on tract number 1980 of 4079 tracts\n",
      "I am working on tract number 2000 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2010 3.0 2 90.0 0.6987 285132.9\n",
      "we have 2 non-opposing shorted wedges for tract no 2010\n",
      "I am working on tract number 2020 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2020\n",
      "I am working on tract number 2040 of 4079 tracts\n",
      "I am working on tract number 2060 of 4079 tracts\n",
      "I am working on tract number 2080 of 4079 tracts\n",
      "I am working on tract number 2100 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2102 13.0 0 92.8 0.5373 189064.5\n",
      "I am working on tract number 2120 of 4079 tracts\n",
      "I am working on tract number 2140 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2149 4.0 1 90.0 0.6977 198775.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2149 5.0 1 90.0 0.6825 198775.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2149 10.0 3 67.7 0.5625 246461.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2149 11.0 3 67.7 0.5051 246461.9\n",
      "we have 2 non-opposing shorted wedges for tract no 2157\n",
      "I am working on tract number 2160 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2161 7.0 3 55.4 0.5176 205087.7\n",
      "I am working on tract number 2180 of 4079 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2190 1 213708.97882692626 0.5661\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2190 1 85701.45664057968 0.283\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2190 1 213707.24123973082 0.566\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2190 1 119029.31361402488 0.4245\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2190 1 178001.70784799758 0.4953\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2190 1 205130.71540515125 0.5307\n",
      "I am working on tract number 2200 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2215\n",
      "we have 2 non-opposing shorted wedges for tract no 2217\n",
      "I am working on tract number 2220 of 4079 tracts\n",
      "I am working on tract number 2240 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2259 11.0 0 85.8 0.7337 204969.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2259 12.0 0 85.8 0.7012 204969.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2259 13.0 0 85.8 0.6701 204969.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2259 14.0 0 85.8 0.6404 204969.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2259 15.0 0 85.8 0.612 204969.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2259 16.0 0 85.8 0.5849 204969.5\n",
      "I am working on tract number 2260 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2278 7.0 0 92.2 0.4487 124014.9\n",
      "I am working on tract number 2280 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2284 3.0 0 90.0 0.2887 97647.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2285 6.0 1 51.2 0.2438 125734.3\n",
      "I am working on tract number 2300 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2306 3.0 3 90.0 0.2294 92921.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2316 9.0 1 90.0 0.2017 173737.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2316 10.0 1 90.0 0.2181 173737.5\n",
      "I am working on tract number 2320 of 4079 tracts\n",
      "I am working on tract number 2340 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2354 8.0 3 90.0 0.5838 231270.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2354 3 231270.0301883088 0.6787\n",
      "I am working on tract number 2360 of 4079 tracts\n",
      "I am working on tract number 2380 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2380 9.0 2 90.0 0.7036 213036.1\n",
      "I am working on tract number 2400 of 4079 tracts\n",
      "I am working on tract number 2420 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2423 3.0 1 90.0 0.5077 156712.9\n",
      "I am working on tract number 2440 of 4079 tracts\n",
      "I am working on tract number 2460 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2476 5.0 3 90.0 1.0654 198571.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2476 6.0 3 90.0 1.043 198571.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2476 7.0 3 90.0 1.021 198571.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2477 1 261030.717136397 1.1194\n",
      "I am working on tract number 2480 of 4079 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2486 1 345580.91770220513 1.0842\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2486 1 54812.379176937975 0.5421\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2486 1 380223.423345438 1.1291\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2486 1 115367.57408910821 0.8356\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2486 1 192139.01634794104 0.9823\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2486 1 303110.1774205138 1.0557\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2486 1 352335.03935445374 1.0924\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2486 1 335364.70548048365 1.074\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2486 1 322374.80671427184 1.0649\n",
      "we have 2 non-opposing shorted wedges for tract no 2495\n",
      "we have 2 non-opposing shorted wedges for tract no 2497\n",
      "I am working on tract number 2500 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2505 5.0 3 67.7 0.6808 230494.1\n",
      "I am working on tract number 2520 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2521\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2535 2 238359.91110867108 0.7096\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2535 2 64477.55081754108 0.3548\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2535 2 238363.3478212368 0.71\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2535 2 108950.3372556024 0.5324\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2535 2 215230.76163613296 0.6212\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2535 2 135063.0756806867 0.5768\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2535 2 184142.72478718977 0.599\n",
      "13 yoyos for tract,wedge,wedgePop,r= 2535 2 203825.32654419908 0.6101\n",
      "I am working on tract number 2540 of 4079 tracts\n",
      "I am working on tract number 2560 of 4079 tracts\n",
      "I am working on tract number 2580 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2597 5.0 3 55.3 0.4967 212588.7\n",
      "I am working on tract number 2600 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2601\n",
      "we have 2 non-opposing shorted wedges for tract no 2602\n",
      "we have 2 non-opposing shorted wedges for tract no 2603\n",
      "we have 2 non-opposing shorted wedges for tract no 2604\n",
      "I am working on tract number 2620 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2626\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2635 6.0 1 36.2 1.4298 345816.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2635 7.0 1 36.2 1.3216 345816.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2635 12.0 1 36.2 1.4472 345816.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2635 13.0 1 36.2 1.3376 345816.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2635 18.0 1 36.2 1.4771 345816.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2635 19.0 1 36.2 1.3653 345816.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2635 1 345816.7656519377 1.4208\n",
      "I am working on tract number 2640 of 4079 tracts\n",
      "I am working on tract number 2660 of 4079 tracts\n",
      "I am working on tract number 2680 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2696 7.0 3 72.4 0.4638 270452.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2697 7.0 3 67.5 0.4929 277229.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2698 7.0 3 61.1 0.4231 205558.2\n",
      "I am working on tract number 2700 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2701 5.0 0 109.9 0.6297 181022.4\n",
      "we have 2 OPPOSING shorted wedges for tract no 2703\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2704 4.0 0 90.0 0.6426 210073.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2704 5.0 0 90.0 0.6027 210073.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2704 6.0 0 90.0 0.5652 210073.0\n",
      "we have 2 non-opposing shorted wedges for tract no 2711\n",
      "we have 2 non-opposing shorted wedges for tract no 2714\n",
      "we have 2 non-opposing shorted wedges for tract no 2717\n",
      "I am working on tract number 2720 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2727 11.0 0 106.4 0.9132 200351.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2727 12.0 0 106.4 0.888 200351.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2727 13.0 0 106.4 0.8635 200351.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2727 14.0 0 106.4 0.8396 200351.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2727 15.0 0 106.4 0.8164 200351.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2727 16.0 0 106.4 0.7938 200351.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2727 17.0 0 106.4 0.7719 200351.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2727 18.0 0 106.4 0.7506 200351.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2727 19.0 0 106.4 0.7298 200351.2\n",
      "I am working on tract number 2740 of 4079 tracts\n",
      "I am working on tract number 2760 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2775 4.0 0 83.5 0.4514 243789.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2779 2.0 0 90.0 0.3908 208317.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2779 3.0 0 90.0 0.3689 208317.3\n",
      "I am working on tract number 2780 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2793\n",
      "I am working on tract number 2800 of 4079 tracts\n",
      "I am working on tract number 2820 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2828 7.0 3 55.7 0.4577 207405.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2828 8.0 3 55.7 0.4335 207405.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2828 9.0 3 55.7 0.4245 207405.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2828 10.0 3 55.7 0.4156 207405.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2828 11.0 3 55.7 0.4069 207405.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2831 6.0 3 70.3 0.6135 211974.5\n",
      "we have 2 OPPOSING shorted wedges for tract no 2832\n",
      "we have 2 non-opposing shorted wedges for tract no 2833\n",
      "I am working on tract number 2840 of 4079 tracts\n",
      "I am working on tract number 2860 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2874\n",
      "I am working on tract number 2880 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2895\n",
      "I am working on tract number 2900 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2915 15.0 0 127.0 1.3884 319041.1\n",
      "I am working on tract number 2920 of 4079 tracts\n",
      "I am working on tract number 2940 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2943\n",
      "I am working on tract number 2960 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2969\n",
      "we have 2 non-opposing shorted wedges for tract no 2972\n",
      "we have 2 non-opposing shorted wedges for tract no 2979\n",
      "I am working on tract number 2980 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2982\n",
      "we have 2 non-opposing shorted wedges for tract no 2984\n",
      "we have 2 non-opposing shorted wedges for tract no 2994\n",
      "we have 2 non-opposing shorted wedges for tract no 2999\n",
      "I am working on tract number 3000 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3000\n",
      "we have 2 non-opposing shorted wedges for tract no 3001\n",
      "we have 2 non-opposing shorted wedges for tract no 3006\n",
      "we have 2 non-opposing shorted wedges for tract no 3009\n",
      "I am working on tract number 3020 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3020\n",
      "we have 2 non-opposing shorted wedges for tract no 3023\n",
      "we have 2 non-opposing shorted wedges for tract no 3024\n",
      "we have 2 non-opposing shorted wedges for tract no 3038\n",
      "I am working on tract number 3040 of 4079 tracts\n",
      "I am working on tract number 3060 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3069 6.0 1 90.0 1.2975 240568.9\n",
      "I am working on tract number 3080 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3086\n",
      "I am working on tract number 3100 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3102\n",
      "I am working on tract number 3120 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3127 5.0 0 83.3 0.5372 233253.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3131 5.0 0 88.1 0.5801 194158.6\n",
      "I am working on tract number 3140 of 4079 tracts\n",
      "I am working on tract number 3160 of 4079 tracts\n",
      "I am working on tract number 3180 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3197\n",
      "we have 2 non-opposing shorted wedges for tract no 3198\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3198 4.0 0 86.0 1.3986 447330.7\n",
      "I am working on tract number 3200 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3203\n",
      "we have 2 non-opposing shorted wedges for tract no 3207\n",
      "I am working on tract number 3220 of 4079 tracts\n",
      "I am working on tract number 3240 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3249 3.0 3 36.1 0.4918 256883.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3254 3.0 0 112.9 0.5228 168256.2\n",
      "we have 2 non-opposing shorted wedges for tract no 3257\n",
      "I am working on tract number 3260 of 4079 tracts\n",
      "I am working on tract number 3280 of 4079 tracts\n",
      "I am working on tract number 3300 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3318\n",
      "I am working on tract number 3320 of 4079 tracts\n",
      "I am working on tract number 3340 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3345 13.0 0 91.6 0.5323 188740.5\n",
      "I am working on tract number 3360 of 4079 tracts\n",
      "I am working on tract number 3380 of 4079 tracts\n",
      "I am working on tract number 3400 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3402\n",
      "we have 2 non-opposing shorted wedges for tract no 3406\n",
      "we have 2 non-opposing shorted wedges for tract no 3416\n",
      "I am working on tract number 3420 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3431 2.0 1 90.0 0.4607 231460.4\n",
      "we have 2 non-opposing shorted wedges for tract no 3438\n",
      "I am working on tract number 3440 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3441 3.0 3 39.7 0.4594 232380.1\n",
      "we have 2 non-opposing shorted wedges for tract no 3449\n",
      "we have 2 non-opposing shorted wedges for tract no 3450\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3451 5.0 3 55.4 0.9891 301519.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3451 6.0 3 55.4 0.9833 301519.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3452 5.0 3 65.8 0.7853 264948.2\n",
      "I am working on tract number 3460 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3460\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3477 4.0 1 90.0 0.7085 308473.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3478 3.0 1 90.0 0.2189 70951.1\n",
      "I am working on tract number 3480 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3490\n",
      "I am working on tract number 3500 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3512\n",
      "I am working on tract number 3520 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3526\n",
      "we have 2 non-opposing shorted wedges for tract no 3528\n",
      "I am working on tract number 3540 of 4079 tracts\n",
      "I am working on tract number 3560 of 4079 tracts\n",
      "I am working on tract number 3580 of 4079 tracts\n",
      "I am working on tract number 3600 of 4079 tracts\n",
      "I am working on tract number 3620 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3627 17.0 0 102.1 0.8958 309062.0\n",
      "I am working on tract number 3640 of 4079 tracts\n",
      "I am working on tract number 3660 of 4079 tracts\n",
      "I am working on tract number 3680 of 4079 tracts\n",
      "I am working on tract number 3700 of 4079 tracts\n",
      "I am working on tract number 3720 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3733 7.0 3 61.7 0.4544 261145.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3734 7.0 3 61.5 0.4489 273740.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3737 7.0 3 65.8 0.4783 251473.3\n",
      "I am working on tract number 3740 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3754 7.0 3 52.1 0.4461 195932.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3754 8.0 3 52.1 0.4411 195932.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3754 9.0 3 52.1 0.4362 195932.0\n",
      "I am working on tract number 3760 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3767 5.0 0 83.5 0.7236 212793.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3767 6.0 0 83.5 0.672 212793.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3767 6.0 3 96.5 1.0282 227849.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3767 7.0 0 83.5 0.624 212793.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3767 8.0 0 83.5 0.5795 212793.9\n",
      "I am working on tract number 3780 of 4079 tracts\n",
      "I am working on tract number 3800 of 4079 tracts\n",
      "I am working on tract number 3820 of 4079 tracts\n",
      "I am working on tract number 3840 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3849 3.0 0 83.8 0.4693 220812.7\n",
      "I am working on tract number 3860 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3869 4.0 2 90.0 0.6393 193710.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3869 5.0 2 90.0 0.6376 193710.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3869 6.0 2 90.0 0.6359 193710.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3869 7.0 2 90.0 0.6343 193710.6\n",
      "I am working on tract number 3880 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3884 4.0 2 90.0 0.6267 206295.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3884 5.0 2 90.0 0.596 206295.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3884 6.0 2 90.0 0.5667 206295.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3889 4.0 2 90.0 0.6038 210783.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3895 5.0 0 95.8 0.7201 184579.6\n",
      "I am working on tract number 3900 of 4079 tracts\n",
      "I am working on tract number 3920 of 4079 tracts\n",
      "I am working on tract number 3940 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3950 4.0 1 37.8 0.9789 256690.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3950 5.0 1 37.8 0.9661 256690.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3956 4.0 1 40.9 0.7808 281111.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3959 5.0 1 38.0 0.943 286739.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3959 6.0 1 38.0 0.8555 286739.2\n",
      "I am working on tract number 3960 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3961 4.0 1 36.3 0.9687 269424.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3961 5.0 1 36.3 0.933 269424.0\n",
      "I am working on tract number 3980 of 4079 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 3984\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3994 4.0 2 90.0 0.9433 202487.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3994 5.0 2 90.0 0.9099 202487.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3994 6.0 2 90.0 0.8776 202487.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3994 15.0 2 90.0 0.9261 202487.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3994 16.0 2 90.0 0.8933 202487.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3994 2 200905.14974681762 0.8089\n",
      "8 yoyos for tract,wedge,wedgePop,r= 3994 2 74325.7386201685 0.4045\n",
      "9 yoyos for tract,wedge,wedgePop,r= 3994 2 202487.21224939456 0.8772\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3994 2 95544.81099981834 0.6408\n",
      "10 yoyos for tract,wedge,wedgePop,r= 3994 2 172159.77580755448 0.759\n",
      "11 yoyos for tract,wedge,wedgePop,r= 3994 2 201812.93794228646 0.8181\n",
      "7 yoyos for tract,wedge,wedgePop,r= 3997 1 287667.48325992026 1.1271\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3998 4.0 1 36.4 0.9717 267673.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3998 5.0 1 36.4 0.9403 267673.6\n",
      "I am working on tract number 4000 of 4079 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4002 1 312025.67718499375 0.9968\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4002 1 35005.9163503428 0.4984\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4002 1 311995.5437182401 0.9852\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4002 1 88319.93633669979 0.7418\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4002 1 263273.82082637784 0.8635\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4002 1 152285.09682476247 0.8026\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4002 1 200292.61910803552 0.833\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4002 1 238126.88681299123 0.8483\n",
      "we have 2 non-opposing shorted wedges for tract no 4003\n",
      "I am working on tract number 4020 of 4079 tracts\n",
      "I am working on tract number 4040 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4043 9.0 0 120.6 0.8199 234485.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 4043 3 256089.79058866773 0.5077\n",
      "8 yoyos for tract,wedge,wedgePop,r= 4043 3 110800.2801665655 0.2539\n",
      "9 yoyos for tract,wedge,wedgePop,r= 4043 3 256278.05431820964 0.5085\n",
      "10 yoyos for tract,wedge,wedgePop,r= 4043 3 157118.55463242315 0.3812\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4043 3 237161.62638706522 0.4448\n",
      "11 yoyos for tract,wedge,wedgePop,r= 4043 3 203224.53674603713 0.413\n",
      "12 yoyos for tract,wedge,wedgePop,r= 4043 3 183114.4896458251 0.3971\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4053 7.0 0 115.0 0.6439 198477.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4053 8.0 0 115.0 0.6306 198477.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4053 9.0 0 115.0 0.6175 198477.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4053 10.0 0 115.0 0.6047 198477.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4059 7.0 3 70.1 0.4485 220636.5\n",
      "I am working on tract number 4060 of 4079 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4063 6.0 3 60.5 0.4723 208384.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4066 5.0 3 57.8 0.4555 211388.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4068 10.0 0 89.0 0.6553 195680.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4068 11.0 0 89.0 0.6486 195680.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4068 12.0 0 89.0 0.642 195680.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4068 13.0 0 89.0 0.6355 195680.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 4068 14.0 0 89.0 0.629 195680.9\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0004902766193386\n",
      "Average and max number of wedgePop loops per tract were:  5.784260848247119 40.0\n",
      "min,average,max of Home District areas were:  0.0 0.18129500977866808 0.7724780914809968\n",
      "calculated statewide vote was 0.3191572456914419, should have been 0.3297095295290766\n",
      "calcd statewide Hispanic pop was 0.0973314380351871, should have been 0.10222291816625761\n",
      "calcd statewide Black pop was 0.2839353463886024, should have been 0.29157530670572074\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.3140475606766364\n"
     ]
    }
   ],
   "source": [
    "#3/13/22 - from HD2, but using coeff1, coeff2 (unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0004902766193386\n",
      "Average and max number of wedgePop loops per tract were:  5.784260848247119 40.0\n",
      "min,average,max of Home District areas were:  0.0 0.18129500977866808 0.7724780914809968 for  MD\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start MD HD1 4:54 ends 6:47\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MD 14Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"14Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "585 0.95 ( -77.65076252972523 39.61972760313935 ) 0.45717983539988044 t, pop/target,(x,y), pctR\n"
     ]
    },
    {
     "data": {
      "image/png": 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LlYXN/K7WjSfp29L55v5vOLj8IO2fbK/li5VSPmdlTfpqkehn/HYGaVvSuOV/t5A0PMnqcJRS1VBp140pNojNtzPfVYuum0BnIKbYYAu0YVzG6nCUUtWQI9oBBkueyq8Wib7Xa72IqBvBlGFTGN1oNDOfncnx/cetDkspVY1YWQahWnTdRNaP5LEtj/Hzjz+z/K3lLH19KaveW0Wn33ciMCSQAEcAtdvWpnab2uz4fgczfjeD0LhQrn/pehJ7JlodvlLKD5xaBiE60bcPbFaLRA/uJ9Oa9m1K075NydiZwcQ7JzLnj3POWAkwENM0hqzDWXza91O6vtiVtg+1JaxmmCVxK6X8g5U16atNoj9VTOMYRq4aSf7xfIwx5J/I58CSA6SsSyGmSQwtBrWguKiYb4Z+w7w/z+Onf/9Ex990pFH3RoTXCSemSYzPb6Yopao2K2vSV8tEDyAiOKLcNaKd0U6iGkTRYlCL09YZ9M0g0remM+vZWSz4+wIW/M1dfj+qURQtB7fEFBsiEiJo0LUBAY4Ads7Yyf7F+0lZl0JUgyhuHXerfhNQSgHW1qSvtom+vGKvjGXQlEHkpOaQsi6FzN2ZbJ64mYUvL3RPv3KGiIQI4lvEs2PaDhaPWkzv13r7PmilVKVT2nWjN2MrsdD4UBrf2BiAtiPbknsslyPrjnAy/SRF+UUUZBWQ2DORmCYxALxa81UKcwqtDFkpVYkEOAIIDAnUFn1V4ox20vD6hud8Pyg8iJNpJ30XkFKq0rOqDEK1GEdvhcQbE9kyeQtrxq2xOhSlVCVhVRkETfRecuMrN9KgSwOmPTaNo9u1WqZSqqQMgrbo/UdwRDC3jb+NoLAgPun9CYUntb9eqerOqpr0mui9KKphFHdMuIPMPZl8fOPHbPtuG6ZYa+0oVV05YqyZTlATvZcl9kzklrG3cHzfcT6/9XPeafYOy99ZTlF+kdWhKaV8zKoWvY668YE2D7Sh1f2t2PL1FpaNXsYPT/zAwpcXUr9TfWq1rkWroa2IqBthdZhKKS9zxjgpyi2iKK+IAIfv0q+26H3EHminxd0teOCnB7hv1n3Uu64eKWtTmPPCHEY3Gs2iVxZZHaJSysusKoOgLXoLJPZIJLGHuyrmsd3HmPnMTGY/N5vCnEKuf+l6RLSOjlL+6NQyCOG1w312XG3RWyy6UTQDvxhI6wdas+BvC/j+0e8pyCmwOiyllBdYVZNeW/SVgM1u45Yxt+CIdrDk1SXsmrGLe767RycxV8rPnFqT3pe0RV9JiE3o9e9eDFswjKxDWcx6bpbVISmlKphVNek10VcyGTsyKMorIqy2ljdWyt9YdTP2goleRBwislxE1onIJhF5qWR5KxFZIiIbROQ7ETlrfKCI1BORuSKypWTbp7xxEv5i1ZhVfPvAtzTu3Zg+b/axOhylVAULjggGqZwt+nyguzGmFZAE9BGRjsBY4DljTEtgMvCMh22LgN8aY64COgKPiUjzConcz+Rm5DL9qek07t2YQVMGEegMtDokpVQFE5vgjPZ9YbMLJnrjll3yMrDkxwDNgAUly2cCd3jY9rAxZnXJ71nAFqBuBcTtd/Yu3EtRXhFdnu9CQLDeI1fKX1lR2KxcffQiYheRtUAqMNMYswzYCNxassqdQL0L7KMh0BpYdo73R4rIShFZmZaWVr7o/YhxuWvg+PJpOaWU71lRk75cid4Y4zLGJAEJQHsRaQGMwN0VswoIB845+FtEwoCvgF8bY06c4xhjjDHJxpjkuLjqMazQFBtS1qWw5PUl/PibH3HWcBJ3dfU4d6WqKytq0l9U89EYkyki84A+xphXgV4AInIFcJOnbUQkEHeS/9QY8/XlhesfXIUu5rwwh7Xj1nIy3T0LVc1WNRn45UCCQoMsjk4p5U3OGCfHdh7z6TEvmOhFJA4oLEnyTqAn8IqIxBtjUkXEBrwAvOthWwHeB7YYY16v4NirrNnPz2bJq0toPrA5TW9uSmKPRCIStKiZUtWBFV035WnR1wbGi4gdd1fPl8aYqSLylIg8VrLO18AHACJSBxhrjOkHdALuAzaU9PEDPG+MmVaRJ1GVpG9LZ+nrS2kzsg23vHeL1eEopXzMGeMkLzMPU2wQm2/qWl0w0Rtj1uO+iXrm8tHAaA/LDwH9Sn5fBGiFrlMsenkRAY4Auv+tu9WhKKUs4Ix2YooN+SfycUQ5fHJMfTLWh1yFLrZO2crVg64mND7U6nCUUhaw4ulYTfQ+dGjFIfKP59O0b1OrQ1FKWcSKwmaa6H1o+TvLEbvQoFsDq0NRSlnk1Jr0vqKJ3odSN6SS2COR0DjttlGqurKiJr0meh+KqBdBTlqO1WEopSykXTd+LrJBJJm7MzHFxupQlFIWsaImvSZ6H2rYrSF5mXls/Hyj1aEopSwS4AggwBmgo278VfOBzanVuhZf3/s1YzuOZdEri8jYmWF1WEopH/N1BUtN9D5kC7AxbN4wrv/r9Zhiw+znZvN2k7cZ13kcB5YesDo8pZSP+LomvSZ6HwuOCKbbi914cPmDPLXnKXqO6snxfcf5sNuH7Fu8z+rwlFI+oC36aiSqQRSdnunEw2sfJqxWGD888YPeqFWqGvB1YTNN9JWAM8ZJ95e7k7ImhdVjV1sdjlLKy3xdk14TfSXR8p6WJHRM4IcnfiDrcJbV4SilvEi7bqopsQk9R/XEVeBizbg1VoejlPIiR7SDwpOFFOUX+eR4mugrkQZdGtD0pqb89O+f2D51u0/H2SqlfMfXZRB0JupKptervRjffTwTbpkAQGjNUGKvjOWKm68gsWci0Y2jCQ4PtjhKpdTlOLUMQlitMK8fTxN9JRN7ZSxP7X6KPXP3kLIuhYwdGRxedZiZz8wsWyc6MZoazWoQ3TiayPqRAITVDCPveB75J/IpLiymKL+I8NrhNB/YnPA64VadjlLKg7IyCD761q6JvhIKCA6gSZ8mNOnTpGxZ5p5MDiw9QMbPGaRuSOXojqPsW7SPgqwCj/uwBdgoLipmxm9n0PftvrQe0Rp7kN1Xp6CUOg9fFzbTRF9FRDWMIqph1GnLjHFPRyYi5KTmEBgaSEiNEGyBNkSEnTN2Mu8v8/j+ke+Z/9J8arepTa3WtWj/eHuffF1USnnm65r0muirMBHBEen+wARHnN1v37hXYxJvTGTnjJ2s/O9KMvdksuOHHfz06k90faErXZ7v4rPJiZVSv9CbsapCiQhNejehSW93N9DRHUeZ++Jc5r44l/2L9zPgkwGE1AixOEqlqhdHpAPEdy16HV5ZzdRoWoM7JtzBTe/exO45uxnTdgzL3lpGyroUil3FVoenVLUgNsER5bsyCNqir4ZEhOSHkqmVVItpj01j+lPTAQiJDaHz853p+FRH7dJRyst8WQZBE301ltAhgZErR5LxcwYHlh1g/UfrmfH0DAKCA2j3aDurw1PKr/myDIJ23ShimsRwzb3XMHjaYEJrhrJ79m6rQ1LK7zmjnT4bR3/BRC8iDhFZLiLrRGSTiLxUsryViCwRkQ0i8p2IRJxj+z4isk1EfhaR5yr6BFTFWfC3BeQcyaFxn8ZWh6KU36tsLfp8oLsxphWQBPQRkY7AWOA5Y0xLYDLwzJkbiogd+D+gL9AcuEdEmldQ7KoC5aTl8NOrP3H1XVfT5ldtrA5HKb/ny5r0F0z0xi275GVgyY8BmgELSpbPBO7wsHl74GdjzC5jTAHwOdD/sqNWFe7I+iMU5hTSckhLRHx/IzbveB7G6KQrqvoovRnri8mGytVHLyJ2EVkLpAIzjTHLgI3ArSWr3AnU87BpXWD/Ka8PlCzzdIyRIrJSRFampaWVM3xVUWq1qoU92M78v8z3+TDLNR+sYVSNUXz34HcUF+kQT1U9OGOcmGJDfla+149VrkRvjHEZY5KABKC9iLQARgCPicgqIBzwVHTFU9PQ458vY8wYY0yyMSY5Li6uXMGrihMSG0Kv13pxePVhn92MNcaw6JVFfDviW4zLsOb9NUy6exJFeb6p0a2UlXxZBuGiRt0YYzKBeUAfY8xWY0wvY0xbYAKw08MmBzi9pZ8AHLq0UJW3tflVG5wxTn544gc2fLaBvQv3eu1Yptgw47czmP3cbFoMasEL+S/QZ3Qftny9hdcTXtda/Mrv+bIMQnlG3cSJSFTJ706gJ7BVROJLltmAF4B3PWy+AmgqIo1EJAgYBHxbQbGrChYQHMCAjweQk5rD1/d+zfgbxnNoVcX/XXYVuJh832SWvrGU9k+05/ZPb8ceZKfDkx0Y8MkAco/m8mG3D3VKReXXfFnBsjwPTNUGxpeMoLEBXxpjporIUyLyWMk6XwMfAIhIHWCsMaafMaZIRB4HfgTswDhjzKaKPw1VUZr2a8rwhcOZ+6e5bJ28lf8l/4+a19QktGYoLe9tSVFuEWIXivKKsAfZCY0PpU7bOoTVCitXGeSC7AK+HPglO3/cSfeXu9P5uc6n3fy95t5rCKsZxue3fc64TuO4b8Z9xDSJ8eYpK2UJX9akl8o40iE5OdmsXLnS6jCqvfRt6WydvJUd3+8gfVs6J9NOnnNdsQlN+jbh9k9vL6uoeaactBw+u+kzDq86zM1jbqbNA+cexnlwxUE+7fspNruNe6ffS+3WtS/7fJSqTLIOZfF63de56d2bSH4o+bL3JyKrjDEed6SJXpVLsauYw6sP44hyEOgMxB5sJ3NPJkV5RaRtTuPYrmMsfX0pDa9vyOBpg7EHnt66P7brGJ/0/oQTB04w8IuBNLu12QWPmb4tnU96fULusVwGTRlEoxsaeev0lPK5wtxCXg55me4vd6fLH7pc9v7Ol+i11o0qF5vdRt12p4+MDY0LBdyTmgPEXRXHlOFTmP38bHr9u1fZeodWHeKzfp9RXFTM/bPvp951nkbini22WSwjfhrBJ70/4dM+n3L7Z7fT/A593k75h0BnIAGOgMpxM1ap8koalkTS8CSWvLqE4/uPA7Bzxk7GXz+eAEcAIxaPKHeSLxVRN4LhC4ZTJ7kOE++cyMr39Jue8h++KoOgiV5VqE6/7wTAjKdnsHb8Wj676TOiE6N5YMkDxF4Ze0n7dMY4uW/mfTTt15TvH/6e+X+br0/RKr/gqzIImuhVhYq9MpYe/+zB5kmbmTJsCvU712fYgmGE1wm/rP0GhgRy9+S7aXV/K+b9aR4/PPGDTx4dV8qbfFWTXvvoVYXr/FxnHNEO0rek0/OVngQEV8zHzB5op/8H/QmJD2HJq0s4mX6SAR8NKNewTqUqI2eMk8zdmV4/jiZ65RUVMVzME7EJvf7di9D4UGb9fha5Gbnc/fXdBIUFeeV4SnmTM9rJ4dWHvX4c7bpRVVKnZzrR/4P+7J6zm496fMTJ9HOP8VeqsnLEaB+9UueVNCyJu7++m5R1KXzQ5YOykT5KVRXOaCeFOYW4ClxePY4melWlNbu1GUN+HELWoSzGdRpH+tZ0q0NSqtx8Ve9GE72q8hp2a8jQeUNx5bsY13kcB1cctDokpcqlrFSxl+vdaKJXfqF269qMWDwCW4CNse3H+uQGl1KXq7SwmbeHWGqiV37DWcNJYEgggN6cVVVCWdeNl1v0OrxS+YUTB0/waZ9PyTqYxcAvB9K4V2OrQ6o2iouK2TplK/sW7iP/eD41rqxB+8fa65DXcvDVLFOa6FWVl74tnU96f0Lu0Vzu/eFeGnXXKpe+cmDZAaaOnMqR9UcIDAnEEe1g7Ydr2bdwH/d8ew9i8/1E81WJr7puNNGrKu3gioN81u8zEBg6byh12taxOqRqoSiviFl/mMWy0csIrxPOwC8HctWAq8hJy2Hpm0v5adRPzP3TXLr/vbvVoVZqjijf3IzVRK+qrJ0zd/LFgC8IjQtlyIwh1Ghaw+qQqoXUTal8dc9XpG5IJfnRZHr+syeBIYHMeGYGy95cBgIhcSEs/MdCYprGkDQ0yeqQKy1bgI3giGDtulHKk41fbGTyfZOJvTKWIdOHXHbRNHVhxhhWvruSGU/PIDgimMHfD6Zpv6a4Cl18PeRrNn2xiTYj27Bl0hbqd65PQVYB3z7wLVENomh4fUOrw6+0HNEOHXWj1JmWv7Ocr+75ioSOCQxfMFyTvA8YY5j+1HSmPTqNhtc35OH1D7uTfIGLSXdPYtMXm7j+peuxB9rJP5GPI9JB+yfaY1yGXbN3WR1+peaLCpaa6FWVYYxh7p/n8sMTP9DsFvcTsaV9nMq7lo1exvK3l9PxNx0Z/P1gwmqGYYzhq3u+YuvkrfQZ3Yew2mGs+L8VhNUOo/s/urN96nYAgiOCLY6+cnNGe3/yEe26UVVCsauYaY9NY9V7q0gakcQt792CLUDbKb5wbNcxZj07i2a3NqPXa70QcY+kWfbWMrZ8vYWeo3rS4ckOZB3OIrJBJMf3HmfhywtZPXY1zW5tRvvH21t8BpWbI9pB2uY0rx5D/09RlV5RXhGT7p7EqvdW0em5Ttw69lZN8j4054U52AJs3PTfmxARThw8wdI3l/Ljr3+kQbcGXPv0tQCE1w6n6wtdAVjxnxU0v6M5t396O4HOQCvDr/R80XWjLXpVqeWfyOfz2z5nz9w99Hq9F9f+5lqrQ6pW9szfw8bPN9L5uc6E1wknY2cG73d8n5PpJ3FEO+jxcg9s9l/+6LYa2oqIehE06NpAE3w5lU4naIwp+7ZU0TTRq0or+0g2n/b9lNQNqQz4eADXDLnG6pCqlcNrDvPFgC+IbRZbNhfw4lGLKcgu4OH1DxPfIv6sxGQPtNOkdxMrwq2ynNFOXAUuinKLykp4VDRN9KpSOrbrGB/3+pjsw9kM+nYQTfs2tTqkasMUG1b8dwUzfzeTkNgQBk8bjCPKQUFOARs/20iLQS2o2bKm1WH6jVPr3ViW6EXEASwAgkvWn2SM+bOIJAHvAg6gCHjUGLPcw/a/AX4FGGADMNwY4/3ZcFWVlbIuhU/7fIqrwMX9s+8noWOC1SFVeZl7M8nLzKMot4jcY7nkZeaRdyyPvON5BEcEE1E3gpC4ENI2p7Hi/1ZwZN0RGvduzICPBhAaHwrAwWUHKcgu4Oq7r7b4bPzLqfVuIupGeOUY5WnR5wPdjTHZIhIILBKRH4C/Ai8ZY34QkX7AKOD6UzcUkbrAk0BzY0yuiHwJDAI+rMBzUH5k74K9TLhlAsERwdw/+37imsdZHVKVt2/RPj7o8kG5149rHseATwbQcnDL07pmSks/10nWMhMVyRf1bi6Y6I0xBsgueRlY8mNKfkr//EQCh85zDKeIFAIh51lPVXNbv9nKpEGTiG4UzZAZQ4isF2l1SH5h23fbsAXauP3T2wkKDcIR7cAZ7cQR7cAR6SAvM48TB0+Qk5pDZP1I4prHebwpeGDJASIbRBISG2LBWfgvX5QqLlcfvYjYgVVAE+D/jDHLROTXwI8i8iruYZrXnbmdMeZgyfv7gFxghjFmxjmOMRIYCVC/fv1LOBVVla0dv5ZvR3xLnXZ1GPz9YEJqaDKpKLtm7KJ+5/pcfafnLpewWmGE1Qo77z52ztjJlslb6Pibjt4IsVrzRaniciV6Y4wLSBKRKGCyiLTAnZR/Y4z5SkTuAt4Hep66nYhEA/2BRkAmMFFEhhhjPvFwjDHAGIDk5GRzyWekqhxjDFOGTaFW61rcP+t+rWNegbKPZJOyNoUe/+xxznVOHDjBkteXkLEjA2eMk7gWccRfHU/c1XFE1nd/q/pu5HfEXx3PDS/d4KvQq41K0XVzKmNMpojMA/oAQ4GnSt6aCIz1sElPYLcxJg1ARL7G3fI/K9Gr6it1YyoADW9oqEm+gu2a5a4zc66JWA6vOcyEmydw8uhJalxRg0OrDrHuo3Vl7weFBRFRL4Lje49zx4Q79N/HC4IjghGbWNt1IyJxQGFJknfiTt6v4O5r7wbMA7oDOzxsvg/oKCIhuLtuegArKyZ05S+KC4sBaNC1gcWR+J9dM3YREhtCraRaZ71XkF3AhJsnIHZh5MqRxLeIB9xdCGmb00jdlErapjTSNqVx5YArdbSNl4hNcEQ5LO+6qQ2ML+mntwFfGmOmikgmMFpEAoA8SvrXRaQOMNYY06+kL38SsBr3EMw1lHTPKHUmbz0VWF0ZY9g5YyeJPRM9zvS0fep2sg5lcf+c+8uSPLhvDtbvXJ/6nfVema94u1RxeUbdrAdae1i+CGjrYfkhoN8pr/8M/PnywlT+rNjlbtHrtHMVK3VDKtkp2ST2SvT4fmlXQeyVsb4MS3ng7Xo3WhlKWa4orwiAAIc+qF2Rds7YCUDjGz33z5f2t5/Yf8JnMSnPvF2qWBO9spwr3wWAPdhucST+ZeeMncQ1jyMiwfPTls1uaYY9yM6qMat8HJk6kyPa4dWbsZroleWK8kta9MHaoq8oRflF7F2wl8QbPXfbgHti6qThSawZt4bj+477MDp1Ju26UX6vIKsA0BZ9RUrblIYr30W96+qdd71Ov+8EBtaMW4P7IfjLV+wqxhTrozAXo7RFX1H/BmfSJpSyXOHJQkBb9BUpZW0KgMdhlaeKToymUfdGzH9pPqvHrqbRDY1o2L0hTfs2veDTsp7s/2k/n/f/nJPpJ7W09EVwRjsxLkNBVoFXpl7UFr2yXEicu9xBQXaBxZH4j5S1KQSGBhLTJOaC69456U5ufu9mGnRpwM4ZO/l2xLe8Ue8NZjwzg4KcX/5Nco/lMuGWCbxz5TscXH7wrP1snbKVj3p8hD3I/c1s8ajFFXdCfs7b9W60CaUsV/o131XgsjgS/5GyNoVarWqVa8iqM9pJ25FtaTuyLcYYUjemsvT1pSx5dQn2IDs9/tEDV6GLiXdOZM/cPZhiw7Zvt1G3fd2yfWT8nMGXt39JneQ6DPxiIKMbjS77pmalXbN34YhyUKdt5a64eWq9m6gGURW+f23RK58qdhWTnZJ92rKZz8wE0JLE5WSKDQU5BRQXFZ/z/ZS1KdRqff5uG09EhJota5I0PAmAWq3c+1j+znJ2z97Nre/fSnTjaHbO2MmR9UfKhsbuW7QPU2y4bfxtpG1xT3Td+43el3B2bnmZeeyes5tDKw+V3axPWZtC6sbUs/qx87PyydyTWfY6dVMqi0ct5ushX/Nxz4/5X/L/2Dxp8yXH4gvernejLXrlM8YY3r/2fQ6tOESj7o2IahRFzpEcMnZkkNAxAUeUw+oQq4Qvbv+CbVO2ASB2IcAR8MtPcAC2QBsFWQUX7J8/nx3TdmAPstOkbxPyjuex4K8LaNy7Ma2GtiInNYdZz87i3VbvEuAMoM2v2rhvpAvENIkhPysf4Jx/iM4n63AWC/+xkNX/W132DS8wNJDabWqzb+E+AELjQ4lOjMZV4ELswvG9x8lJzeHe6feScySHb4Z+U7Zd/S71OXHgBN8M/YYazWpU2pmxtOtG+Y0fn/6RQyvc0xEc3XGU9K3p2AJttH+iPb1fv/TWX3WTvjWdmtfU5KqBV1GUV0RRXhGufFfZ70V5RdRqVYsrbr7iko9xbOcxohpGERwezPbvt5OXmce1v70WEaHT7zvRtF9TUjemsnPmTpa/sxwM2IPsmGJD3FVxOGs4WfDXBTTt17RcN9mPbj/K8neWs3rsaooLi0kankTzgc3Jz8pn2zfbOLL+CEkjkqh5TU0OLDlAzpEcgiODyT36y/R7E26ZUFY3afD3g2ncuzE2u42sw1m8lfgWS15bwm0f3nZR16HwZCH2IDu2AO92fni7VLEmeuUzpQ/uDJkx5JxPa6oLK8wppN619ej2YjevHePk0ZMER7pHf5SWSDi08lDZv1t8i3jiW8TTYlALjqw9wuHVh3EVuMjck0mNK2rQf1x/Pu//OXNemEOvf/c653FMsWHS3ZPYPGkztkAbLQe3pNufuhGdGF22TvM7mp++0VOcJS8zzz2R/MZUmg9sTkLHBGx2d3IOqxWG2IRNX24iODKY0PhQOj/Xuez98/mgywekb03n0U2PEtUw6oLrn092SjZbJm8h50gOxa5irhpwFbXb1Aa060b5gaL8InbP3k3GzxkA7m4ATfSXrCCngMBQ70wiXUpEyp5viGkcQ2LPRBb+YyGNezU+68Zmj3/2YMZvZ1CvUz1imrpH+TS7tRltH27LkleXsHfeXvp/0P+0wmmlslOy2TxpM/YgO49sfIQaTWtcUryOKAcjFo8AObs4nogQe2Ush1cfZsU7KzDFhprX1KTZLc3Oub/SCdJLp0+cMnwKQ+cOvaTYAHbP3c1H3T86bdmasWt4+tDTiAiBoYHYAmxe67rRm7HKq/Yt3sdbiW/x2U2fsW78Ouok1+GqgVdZHVaV5ipwlQ1h9JbDqw9Tv+sv1Stv++g2AhwBLPzHwrPWbdyrMY9seISb3735tCTb+7XedPljFw6tPMTaD9d6PE5YrTDqdqiLq8DF7OdmX1bMYpNzVkAdvnA4z6Q9w/MnnycwNJCdP+485352z93NX+1/5YfHfwCgab+m7Jm355LuOZQ6dZjrk7ue5MZ/3+hu4X+9xR27CM4Y79W70USvvOb4/uN83PNjAkMCuee7e3j22LM8uOJBOj6l09FdDnuQ3etDUUsnGykVXjucmMYxFzVkMjAkkO5/705EQgQHlx/0+NSn2IQRi0YAsGf+nsuO+3yxhMSGEBAcQGKPRFaNWcXUh6eyf8l+jDEcXnOYBX9fwOhGo8ta3mG1whg2f1jZt6clbyy55ONv/247AOF1wwmJDeGaIdfgiHIwceBENk3cBHi3VLF23SivyE7J5vP+n1NcVMx9M++77P5N9YugsKCybhVvSeiYwKYvN1FcVIwtwMahlYdI35bOFTdd/A3eLi904fuHv2f287Pp8bJ7SsM9c/dwMv0kTfs1ZeE/3d8SLmXfl6LvO31x/snJuvHrWPWe54JuMU1jeGL7EwDufnQDs34/i6CwINo90u6ij7niPyuo274uv1r2KwCCw4N5fNvjfND1A2b8dgZXDbgKZ7T36t1ooldeseK/Kziy7gh3fH6HJvkKFlYrjOwj2Rde8TI07deU1f9bzYr/rCBzbyZLX19KaM1Qur7Y9aL31fbBthxefZjF/1rM0a1HCasdxsr/uieai28RT+rGVBJvTKTXa+e+aVuRIutF0v+D/vR+szfbvt3G0teX4ohyEFEvgpC4ENr8qg1xV/3yTEdQWBC3f3Y7RflFTHt0Gmmb0ujzZp9yj8RZ8sYS0jal0eHXHU5bHhofSvIjyfz46x8Z22EshbmFXvumpoleeUXWoSycMU6uvlOnn6toYbXCvF5t8opbriChYwLTn5oOQPKjyfR4uQeOyIt/1kFsws3v3kyNpjWY88IcXPkukoYlEdcijpm/cz8s13pEa0JiQyr0HC7EEemg1X2taHVfqwuuaw+0c9eku5j57EyWvbmM2m1q03rEWfMxlTHFhj3z92APtDP/pfnEXhVL1xfO/iPZ4ckORNSN4IcnfyD7cDZRjaIu55TOSRO98ooaV9TgZPpJco/llg0dUxUjrFaYx1ozFclmtzHkxyFs+nITNa+peVq5g0shIlz3u+todX8rslOyiW8Zj4gQHB7M1IemeqWQV0WzB9np/Xpvds/ezbLRy0ganuTx5q8pNkwZPuW0SdZv+s9NhNQ4+w+ZiNB8YHMSb0xkzgtzKMz2TtkITfTKK2KbucdeH91+lIQOCRZH41/C64aTk5pDQXZB2SxR3hAcEUybX7Wp0H2GxocSGh9a9rrtyLa0ebBNlZkvWETo8GQHvnvwOw4uP3jWZ9tV4GLK8Cls+GwDrR9oTa3WtYhtFkujHo3Ou19HpIN+b/c77zqXQxO98orSMdO7Zu26pESfk5bD1m+20np4a68/lXi5UtamcGTDETAQVjuMgOAAGnRt4JVjpW5K5fDKw2Dg4PKDNOp+/gRSFVSVJF/qygFXMvWhqXwz9BsSb0wksn4kwRHBNOnThKVvLmXDZxtoM7LNWcNNraSJXnlFdGI08S3iOfDTgYveNm1zGv+5+j8A2AJstB5+7r5QqxXkFPBhtw/JP5F/2vI/5v3RK/X1Z/9hNtunbqfd4+1IuFa/KVkhpEYI8S3jObLuCEe3HS1bHhgSWPaHN/nh5EqT5EETvfKioPCgsuqGF2P33N1lv59MO1n2e0F2AbnHcomsF1kh8XlyePVhFo9aTFB4EG0eaENCx3Mn02JXMbP/MJv8E/nc9tFt1G5dm68Gf0XqhlSyDmad9hh/RcnLzKN229pe/ZqvLuzm925m/+L9tH+8Pa4CFycOnODjXh+zfep2EPdzB5WJJnrlFcd2HSN967nHXecdz8MWYCMo9Ow+5tIiVQCzn59N8iPJ5GXm8X7H98k6nMX1f7mebn+q2Dovxa5i1oxbw/SnphPgCKC4qJg1Y9dQt0NdOjzVgatuv+q0FnrW4SwW/XMRy99eTusHWpeN3Ljrq7v4X7v/8Z8W/yH54WR6vdqrXDXhPVn57kr2L95P3Y51CasZxrYp29i3cB9d/tilQs5ZXbqEDgllXZL2IDuxV8YyZPoQ5r44l/ZPtL+k2bm8Sbw1R+HlSE5ONitXrrQ6DHWJNn25iW+GfoMtwD1y49R5S12FLha/spgFf1uAPchO+yfbc93vrjttZM7mrzYzceBEOvy6A8veXAa4R5rkn8in8GQhEfUi+M2+31RIrMWuYpa+uZQ1Y9eQvjWduh3qMmjKIAJDAlk3fh3L3lpGxo4MHNEO2o5syxU3X0FhbiGf9PoEgJb3tuT2T24/bZ9Htx9l7p/msumLTQz8YiBX3+V5iGnhyUIm3DKBzL2ZPPnzk2e9//5173NgyeldX/W71GfIj0MIdHq31o2qekRklTEm2dN72qJXFSplXQqTBk0i7qo4hswYQkTdiLL3Dq08xLcPfMuR9Ue4csCV2APtLHp5EevGryP54WQa92pMWO0wohOjsQXacOW7aH5nczZP3Ex4nXDumHAHUx+aetHjrfct2keNZjUIjQs9671Zz85iyWtLiGoUxe2f3U6LQS3K+lbbP96edo+2Y+fMnaz4vxUsHrWYxa/8Mj1eRL0I+n/Q/6x91riiBrd/ejupG1L56dWfPCb6IxuO8G6rd+E87az6XepzaOUhHt34KEfWHyGyQSR1213eMEdVPV0w0YuIA1gABJesP8kY82cRSQLeBRxAEfCoMWa5h+2jgLFAC9wf6xHGmEsvGqEqtUBnIBhIfiS5LMkXnixk3l/mseS1JYTVCuPub+7myv5XAnDdM9fx7QPfMvfFucx9ce5p+zqx/wT3fHcPxhhEhJ+n/8zR7Uc5uv0oS95YQpsH2pSNv94yeQur3l1F5+c707BbQ+a8OIdlby6jKK+I4qJiwmqF8cTPT5zVVbT1m600vakpg6cO9ng+YhOa9G5Ck95NyD2Wy0c9PiJlTQqdn+/MopcXMf+l+XT/e/eztrPZbcReFUv6lvSz3jPGMP8v88uS/LmeHG58Y2N+GvUTR3ccpfnA5h7XUao8yjNuLR/oboxpBSQBfUSkIzAKeMkYkwT8qeS1J6OB6caYK4FWwJbLDVpVXqWPcGcdzgLcrel3W73LT//+idYPtObRTY+WJXmAOsl1eGjtQ9w7/V6u/d21p+2rxeAWwC/D7wKcAThrOAmvE86Mp2fwRr03mP+3+RxadYiv7vmKnTN2Mv768Uy8ayKLXl5EQbZ7uj17kJ3slGw2fLrhrHhzUnPKXaLBGe2k83OdAXeN9BaDWrB41GIKcz0/5OIqcGELPPt/se1Tt7Pl6y3c8LcbsAXYys7zTPU71yfAEcCumbvKFZ9S53LBFr1xd+KXFtYILPkxJT+l38sjgUNnbisiEUBXYFjJvgoA71ZjUpbKSc0BYO0Ha6nbri6T7p5EREIE98++/5xjvkV+aTV3+UMX0rakUSup1lmt74bdGvL79N8DcHDFQRb/azHz/jSPeX+aR0RCBMMXDmf9J+uZ/9f5AHT5YxdaDW1FTJMY3mv9Hiv+b8VpD+fsXbiXgqyCixodU/oNIu94HknDk9j4+UZ2zdxFs1vPrm0eFBZ0VpGqovwiFv5jIaHxoTS/szlzX5xLdCPPxw9wuMfj75xx7pK6SpVHuZ5EERG7iKwFUoGZxphlwK+Bf4vIfuBV4A8eNk0E0oAPRGSNiIwVkbM7St3HGCkiK0VkZVpa2iWciqoMGl7fkLYPtSX7cDZfDPiCWkm1eHDlg+V+sMcZ46R+p/oeR+Ocqm67utw56U76vt2XwJBA+n/Qn6iGUXR9oStP7X6KX+/7Nd3/3p0aTWsgIrR7rB1H1h85rXW8bvw6bIE2Wj9Q/nH6pX8Utn+3nYY3NMQebGfvgr1nrVfsKmb/T/tPq0N+4sAJPrvpMw4uO0jfd/oS3ch9L+Lojl/GYhfmFrJn3h4yfs4gY2cGib0SSd+SzokDJ8odo1JnKtfNWGOMC0gq6W+fLCItgJHAb4wxX4nIXcD7QE8P+28DPGGMWSYio4HngBc9HGMMMAbco24u8XyUxcQm3PTfm6jfpT77f9pPz3/29FodExFx3zB9rN1pD6ecegO41DVDrmHxK4uZ9vg0Hln/CKbYsOnLTbS8p+VFFeqqcUUN4lvEc3T7UXIzcnHluwirffZQuu1Tt3N87/GyuXD3LdrHJ70/odhVTP8P+5cVe6vfub57GGe7uuQdz2PxvxaXzcQF0O4xd0ncPfP2cM2Qa8odp1KnuqhRN8aYTBGZB/QBhvLL7I0Tcd9wPdMB4EDJNwCASbgTvfJjIsI1917DNff6JjGV5wnEQGcgfd/qy2c3fcamiZsIiQ2hIKvgnP3j5xOREEHWoSxOprsf5vI0mmfr5K04azhpeENDdkzbwfePfk9YrTDun33/afcEbn73Zsa0HcPEOyeWLWt1fytqtanFopcXsWbcGvc52ivPU5aq6inPqJs4oLAkyTtxt9pfwd0n3w2YB3QHdpy5rTEmRUT2i0gzY8w2oAewuQLjV6rcSksGnEw/SeqGVMDd73+xwmqFsXvObmpcUQNHlIO9C/bS6v7TS93mZeaRezSXUTHuMQrhdcIZ+MXAs2781riiBg+ve5iDyw8S2SCS8NrhZesEOgOZ+tBUwPO3FKXKqzwt+trAeBGx4+7T/9IYM1VEMoHRIhIA5OHuykFE6gBjjTGlz2g/AXwqIkHALmB4BZ+DUuVSWhytILuAjZ9vJOHaBAIcF/8oiTPWiavAxZF1RyjILjitGmOpzs91JrJ+JBEJEcReGUvjXo3PeazoxGiPN4RbDW1Vlujtwd6dI1b5t/KMulkPnHW3yhizCGjrYfkhoN8pr9cCHp/WUsqXAhwBBDgCmPeneQAM+GjAJe3n2M/HABh/w3gQ9zMDZ0romHDeOjnlERAcwH0z72Pen+cR1SDqsvalqrfKXf9VqQpkD7TT+w33zdH4lvE06HZppYTrdnA/nVqQXcBdk+7yapG1xJ6JjFg8otLVTlFVi5ZAUNVK8sPJ1OtUD2eM85LLyF5919WIXWj7YFscURc/tZ5SvqaJXlU7NVvWvKztoxOj6fRMpwqKRinv064bpZTyc5rolVLKz2miV0opP6eJXiml/JwmeqWU8nOa6JVSys9poldKKT+niV4ppfycuCeQqlxEJA04ezYH/xQLnD2xqDqVXqPz0+tzYdXhGjUwxsR5eqNSJvrqRERWGmO06Nt56DU6P70+F1bdr5F23SillJ/TRK+UUn5OE731xlgdQBWg1+j89PpcWLW+RtpHr5RSfk5b9Eop5ec00SullJ/TRO8DIvKFiKwt+dkjImtLlt97yvK1IlIsIkketv+LiBw8Zb1+Z65TlVXA9YkRkZkisqPkv2fPtF3Fnesalbx3jYgsEZFNIrJBRM6a9srfP0NQIdfIbz9H2kfvYyLyGnDcGPPXM5a3BKYYYxI9bPMXINsY86pvorTOJV6fUUCGMeZfIvIcEG2MedY3EfveqddIRAKA1cB9xph1IlIDyDTGuM7Y5i9Uk88QXPI18tvPkbbofUjck5TeBUzw8PY951hebVzG9ekPjC/5fTxwW4UHV0l4uEa9gPXGmHUAxpijZyaw6uYyrpHffo400ftWF+CIMWaHh/fu5vyJ/nERWS8i4/zpK+UZLvX61DTGHAYo+W+8l+KrDM68RlcARkR+FJHVIvL782xbHT5DcOnXyG8/R5roK4iIzBKRjR5++p+ymsdWqYh0AE4aYzaeY/f/BRoDScBh4LUKDt/rvHx9/MIlXqMAoDNwb8l/B4hIDw+7r/KfIfD6NfJbAVYH4C+MMT3P935JP+HtQFsPbw/iPK15Y8yRU/bzP2DqJYZpGW9eH+CIiNQ2xhwWkdpA6qVHap1LvEYHgPnGmPSSdaYBbYDZZ+y7yn+GwLvXCD/5HHmiLXrf6QlsNcYcOHWhiNiAO4HPz7VhyYeu1ADAH1u2l3x9gG+BoSW/DwWmeCVC63m6Rj8C14hISEmS6wZsPnPDavIZgsu4Rvjx50gTve+cq1XaFThgjNl16kIRGSsipdX2RpUMCVsP3AD8xruhWuJyrs+/gBtFZAdwY8lrf3TWNTLGHANeB1YAa4HVxpjvoVp+huDyrpHffo50eKVSSvk5bdErpZSf00SvlFJ+ThO9Ukr5OU30Sinl5zTRK6WUn9NEr5RSfk4TvVJK+bn/B81XaE7n6ALYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "368 0.781026812022545 6451 pop,GOP 0.3256859401643156\n",
      "602 0.5585174004775008 1310 pop,GOP 0.7755725190839695\n",
      "607 0.6485437835161472 1406 pop,GOP 0.6187766714082503\n",
      "609 0.725467784309855 2527 pop,GOP 0.6311832212109221\n",
      "645 0.7672625693380719 513 pop,GOP 0.6978557504873294\n",
      "646 0.5970585727530189 1533 pop,GOP 0.6262230919765166\n",
      "649 0.7454316318613227 8784 pop,GOP 0.661316029143898\n",
      "652 0.6629964797068759 9429 pop,GOP 0.616714391770071\n",
      "654 0.626113373132895 1895 pop,GOP 0.5836411609498681\n",
      "658 0.703261367807946 8929 pop,GOP 0.6673759659536342\n",
      "664 0.7750095437776536 9106 pop,GOP 0.671205798374698\n",
      "669 0.7236241341841976 1813 pop,GOP 0.6596800882515168\n",
      "670 0.7937189701436586 8065 pop,GOP 0.6569125852448853\n",
      "675 0.7425796563196332 7745 pop,GOP 0.662750161394448\n",
      "676 0.7670731969637734 8842 pop,GOP 0.6561863831712282\n",
      "677 0.5807766855892906 15 pop,GOP 0.4666666666666667\n",
      "678 0.5896329761181294 346 pop,GOP 0.3208092485549133\n",
      "681 0.7136238932873078 8008 pop,GOP 0.6653346653346653\n",
      "690 0.7929675325578611 9521 pop,GOP 0.6540279382417813\n",
      "764 0.7325537414141633 2258 pop,GOP 0.1612046058458813\n",
      "767 0.71496874005195 2856 pop,GOP 0.13130252100840337\n",
      "768 0.7767590587770152 2230 pop,GOP 0.13901345291479822\n",
      "773 0.7232506061185313 6320 pop,GOP 0.3628164556962025\n",
      "783 0.7545226501005553 5001 pop,GOP 0.43171365726854627\n",
      "784 0.7741532925521677 5939 pop,GOP 0.4010776224953696\n",
      "785 0.7140822510012893 3520 pop,GOP 0.1809659090909091\n",
      "788 0.7801080030599243 5542 pop,GOP 0.3800072176109708\n",
      "794 0.6315475152097166 1190 pop,GOP 0.6210084033613446\n",
      "795 0.7611760128504994 1616 pop,GOP 0.3149752475247525\n",
      "799 0.7077547583137362 1014 pop,GOP 0.621301775147929\n",
      "800 0.567303695343171 823 pop,GOP 0.606318347509113\n",
      "805 0.6112327259053396 10516 pop,GOP 0.5732217573221757\n",
      "806 0.5606066279342508 1812 pop,GOP 0.5838852097130243\n",
      "807 0.554340132928221 1008 pop,GOP 0.7966269841269841\n",
      "808 0.6858787714427563 11419 pop,GOP 0.5740432612312812\n",
      "814 0.693802109403694 10185 pop,GOP 0.5414825724104074\n",
      "819 0.7921612526306326 13439 pop,GOP 0.5142495721407843\n",
      "833 0.5912232693374356 3538 pop,GOP 0.6023176936122103\n",
      "834 0.683292808971076 1215 pop,GOP 0.7086419753086419\n",
      "835 0.6838900114216482 1909 pop,GOP 0.6547930853850183\n",
      "836 0.6222611618190653 2687 pop,GOP 0.71566803126163\n",
      "837 0.5761526074703002 208 pop,GOP 0.6730769230769231\n",
      "840 0.5871311501381232 266 pop,GOP 0.39473684210526316\n",
      "1719 0.685676527760406 1441 pop,GOP 0.6315058986814712\n",
      "1733 0.7628400390644007 3506 pop,GOP 0.6508841985168283\n",
      "1734 0.6244956222599863 1341 pop,GOP 0.4034302759134974\n",
      "1735 0.7023506257245896 1679 pop,GOP 0.6658725431804645\n",
      "1736 0.7182735928238606 1176 pop,GOP 0.5255102040816326\n",
      "1737 0.6888040013862455 1490 pop,GOP 0.7483221476510067\n",
      "1739 0.7081743674388894 1475 pop,GOP 0.6345762711864407\n",
      "1740 0.7393670015819771 2586 pop,GOP 0.6202629543696829\n",
      "1741 0.7506714959329561 2206 pop,GOP 0.770172257479601\n",
      "1742 0.7257020781899992 2746 pop,GOP 0.6959213401310997\n",
      "1743 0.7814679739349452 2289 pop,GOP 0.581476627348187\n",
      "1744 0.7718946908443012 2040 pop,GOP 0.7200980392156863\n",
      "1745 0.6741742844806217 1116 pop,GOP 0.6827956989247311\n",
      "1746 0.6392899824208631 711 pop,GOP 0.4767932489451477\n",
      "1747 0.6306356783348915 2944 pop,GOP 0.5835597826086957\n",
      "1748 0.6763667892004646 2239 pop,GOP 0.5538186690486825\n",
      "1753 0.7514614390693932 279 pop,GOP 0.7419354838709677\n",
      "1754 0.725242641282137 574 pop,GOP 0.7578397212543554\n",
      "1756 0.6716897520724722 1353 pop,GOP 0.4648928307464893\n",
      "1757 0.6928429743228844 1170 pop,GOP 0.7222222222222222\n",
      "1758 0.6623355078336706 1499 pop,GOP 0.6791194129419613\n",
      "1759 0.7575318392966607 1856 pop,GOP 0.5942887931034483\n",
      "1760 0.725778221341116 536 pop,GOP 0.5634328358208955\n",
      "1761 0.7752090377249699 546 pop,GOP 0.6245421245421245\n",
      "1762 0.7147016747554431 2447 pop,GOP 0.7523498161013485\n",
      "1763 0.6465719795305864 2773 pop,GOP 0.4969347277316985\n",
      "1764 0.6912166736495731 400 pop,GOP 0.72\n"
     ]
    },
    {
     "data": {
      "image/png": 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m7c2gZzUterWZXHM6Gls122etpINuNI6kotOq0at05JmMaBw0lC1bllWrVqFSqejUqRNeXl6oVCocHR2ZNm0aXbt2RafTYWtri62tLQkJCZw6dYrKlSuTk5ODh4cH0dHR5Obm4uTkRHJy8kPt6+NKfFY8PcN68rf/36R4p3At9Rr79u2jfbuuqINeZOgntly5Aqa2rbh4JAvVxgzqmqohsgUd2rbDRiPYsO0S7yZc49U33iZRrSHcYEAnIMizPCqhIvLKeUwYkdIEgNFoJCUlhdDQUHQ6HadPn0alUpGbm4tOpyMzM5OsrCwMBgNubm6kpKQQFRWFRqMhMjKSuXPnsmjRIoKDg1m/fj3h4eFcunSpaB/kA+CpGRGlST7WewBGg5Fza85hyjUVdVMAy57K+bXnAShRpQQam1uLOio+X5Fe63phNppZ2HEhuRm5D719sTt+JdBVRZZLBRo2a4khT2KnV5OVJcnNlTjZmrG3tSMzM5OMtAzUuWqiLkaRlJSEm5sbK1as4NSpU5jNZpKSkmjatCmlSpWicuXKrFu3jvr166NWq/n2228JCQmhQYMGNG7cmG7duvHll1+SkpKCi4vLQ+/n48jppNPW47kJc2m9rDVL9i3B09OHT9/4H7bVO2IwQeKqcNx9XDjxZxRzX1+MR0VbQndvQmcWmJFE5ZgZOuEbDLkGTECmlJyIO49Jmsg1FvyNGY1GAFJTU0lOTkZKidFoxGw2o9Fo0Ol05OTkEB8fz5UrVzCZTJhMJjIyLF5wvvjiCyZMmEBERAT29va4urpSqVKlR/bMHhZPzQRgNj2+K4CsxCymBk5lYceFHJl75JHcU0rJkblHOPjzQTKuZtx0fd1b6zg67ygho0Oo9MLt/xHcy7vT7Y9uxJ2IY9NHm+5ejfUu+Xt/FAdjTaw5HI3aoxx6vRqDwYSjo4r0dBPZ2XqqV6+O1sYiOHBycEKn0xEbG0tcXBxnzpwBQKfTYW9vT/369Zk/fz6RkZGEhIQwZ84cPvnkE8LDw5kxYwYLFiwgOzsbtVrNpEmTmDFjxj2p9z4N+Dv7A1DPrR4N9A1QSRVb0raw88Quflp/hPSDK1E7e/HGh0NJPZPBkXPbaPBKIHFRmZApWVymLBUd7MBgYFpZf0oKixlZP2dnJBIB6MrWzDcuA0dHR7p37w5AUFAQJUuWBEClUuHh4UFKSgpGoxFHR0dCQkJwc3OzbDg7OuLj44NOpwPg2WefpVatWpQpU4ZSpUpRunTpR/7sHjRPzQ7V47oCSLuSxvelvree+9bxfej3zLiWQdhPYWwbsw2Atbq1VO9bncYfNMa9vDuXtl3iwI8HaPBOA5qPan7H9ZZrW466b9TlwA8HKNWoFEE9gx54243GTPbsHcbKyAzqtnBkT5iKFStWoAKMRjNCBBJ9pR1q3W/4DPPBLseOqtWqkn0om2rVqrFgwQLCw8OpXLkynTp1Yvny5eh0Otq0acP69espU6YMixYtIiUlhfr16zN06FD0ej2BgYE3OS1TKJySDpYB+GjqUcJ6hxF2NowBGQPYseQ0zlxE6+mPMSmGXJMZW1tbKpSoy6edv6fb4i642prwDtJwbYUBjZSopBk3Gxui8vKoWa4kcw+lorPRkxt1nOtGJ+np6fz1118IIUhPTyc21mJ/qtFoSE21iCRNJhNqtZpz586RlJQEWGwWcnNzSUtLY8iQIRgMBuzs7Ni3b9+TsQGMsgIo9hyZd6TA+R/d/mB2k9lkXLv5rfxeMeWaWPf2Oja8t4HEs4ks7bHUOvj71fej5sCaHPvtGD9U/IFVr69izbA12DjZ0PKrlnd9r3aT2uEd7M3mjzZjzDEWuGY2mkmJTLmvvuzb9zc7d66nVi1bLpw3MCo4k2vXrpKdYkRfzpYTJ07x3nvfk+ueyx+D/yDtYBpHFx8lKSmJNWvWAFCvXj3MZjPr1q2jevXqREVFMW7cOM6cOUNycjJxcXGUL1+eyMhIypUrR6tWrbCxsbmvdj9NXF8ZGUwGvv1zJE4OTnSy6US7WrXJCp1O9uWjOHcZyrZVK0lOTqZJkybY2dljyMwjI8XES0cvkWQyYZaSL6/GcTgzE7VWy8QLpwDINeSAueBvS6vVIqUkPj4elUplbcf11YCNjQ2ZmZnExsaiUqmwsbHh66+/pmXLlqjVaoKCgpgwYQILFy6kc+fO1KhR4xE+sYfH4zci3iOP6wrA0dcRAL2rHoDki8lc3nWZAz8eeGD3iNgawb7J+9gzYQ8/VPyByG0Wj93V+1Sn86+d6TitI29Hvk29N+px6OdDxJ+Mx6eWD1pb7V3fS6VW0ea7NqRGpnJw5kFruinXxLJ+y5hcdjI7v9kJwM5xO5ndZDbn1p67bb2bT18j+PMNbN58hvR0M/bOtiSWe50JJ13RalS4ubmRfS4blxYuaEtqST2TSsWAipQpWQatRkuZMmWoVasW06dPJzs7G3d3d0wmEwcOHECtVjN06FAAtmzZQtWqVSlfvjzz588nJiaGVaseqhurJ5L51d6kRnolso6rWThzIepoNTVr1uTZ9m3Rt2yNbB1E1NlzuLq68ttvv/Hhhx8ihMCOQKRJi9RoqFy1Jo29JSokwpRHmsHyxq92K4mwsccGvfV+OTk5AOj1epydLcoLBoOBS5cuWayujUa8vb0JCAjAbDZjNBr57rvv2Lt3Lzk5OaSkpPDnn38yduzYJ8IH0HWeGhHQ47gCyM3IZcWAFQB4VPLgyp4r1mvXJ4YHgW9tX2ycbbD3tCewXSD7p+4HoFrParhXsBhxO3g50H5Ke0JGhRB3Iu6+LKoDWgVQuklp9kzYQ0CbABLDE/m7z9/WzeHNH27G0ceRCxsucHnXZRZ2WIidhx1lW5Sl68KuqDQ3/x2HLDhErtHMalGNanEN2XDWiHOrtlC9LXLOmxgb9MYlMozqbfw5nrEQR7UPh/Yc4vLlywwfPtz69v/DDz9Y9cRVKhVly5alZMmSTJkyBVdXV9atW8fUqVOpV68eALGxsTcZnSncnhq1X6Xhfi/iOQxAnlMe/pX8yc7OhqV/UKJ2aWIqViZ77wHKlStHdHQ0djoX3u8yDRub2QyftoBctYlna0oWRUBdXxV1/dRM3Z+HOSsVnVcguVHHgIIuKTQaDVlZFpsUNzc3cnNzycjIQK/Xs3PnTvz9/dFqtfz888+UKVOG4cOHW/X/c3Nz2bt37xPlCuLxGhHvg8dxBXBx80XrccKZBABq9K1Biy9aUPu12g/kHlJK8rLzaDuhLUnnksi8lkmPZT14dsazlGtT7qb8du52lA0pi43T/Yk8mn7alNTLqUyrMo3FnRffpBm07JVlXNp6CYC2E9tSqlEpTv1xivCV4YXW5+diS5CfM9ekIxsce5J1JZyKnnpeL3kRvU5DeRlJWRc1ESkdCPB8BoEgKSmJsLAwawCR5cuX8+OPPzJ8+HCrmMDHx4eEhAQSExPZuHGj1eDLZDIREhJCUFAQnTp1uq9n8bQy9PXnadi0IQDtGrXj/LkMbG1tCQhpTuyMX0k7dprBb7xKtaqViImOoYJfTa4knGfSn4fxdRR06/Yig1dmoxaQmQezD1usfmVOOoaoo8j8PQApJR4eHgAkJiaSnp4OWDx7Xtfy0ev1VKpUCbPZTF5eHgMHDqRDhw6YTCZ0Oh0qlQpnZ2c8PT155ZVXiuBpPRyemgngcXMFkR6TzuIXFmNXwo6PMz+m+ejmAJRuWpr02HSi90c/kPusGLCCSaUnsf3z7ZRuUpqTS05yduVZar9au9A37QdF4DOBDDk+hBd+fYEO0zrwwq8vWK8NPjCYnqt6UuvVWjw/+3kavtOQ7n91R6VVEb2v8H57Otpgq1UT/mU71HoHHGt2IOH3T1i56Hc2LJlHzxZ1sc1JxkYjqPXCUFJSUnjppZeYPXs2H3/8MQC9evUiKiqKDRs20Lx5c6Kjo5k1axaOjo4EBgZy9uxZ2rRpg5eXF2q1mm3bthEeHs7YsWOtm4kKd44QgtbNW+Pp6cnmdZvRJ6cAkPTyMNRDP8K9bgsqu/ozvOXPuDl5YGfjgK+LHf0b+2MStmgO76KEvZohdbQcG+JAxseWsOTt+r6Jn38gtipbOjp3pHbt2jg4OFC1alVCQkIYM2YMHh4evP/++3Tp0gU7OzsOHz7M5s2bEULQu3dvkpKSsLGxYeTIkVStWhV7e3tmzpyJr68vc+bMKcKn9mB5akRAj5sriOuboeU7lEdrpyW4fzC7vt3FysErAcjLyKNk/ZL3dQ8pJUd/O4pfPT+MBovnSeChG2tdx7OqJ55VPQHISc3BNcCV5IvJeAd7o9KoqNCxgjWvyWDCnGdG56ArtC4PBxvOXE1Dmx/vwLtuBw6O+cdXYcVKlZjxy2xyDHlUctdQpUoVtm3bVqCOQ4cO0bdvX/bu3WtN8/Pz48CBA+Tm5tK5c2e+++478vLyUKvVqFQq7O3t0ev16PV6FO4etVpNhw4dmDt3LgBpvqWp4uyATl2B4/t/wFCqNy7lL1GyTEkiYk5jzFxOcmY2qGxIzspGSjVqFcQIX9aVfA8pBzH8pfZMiD7F9shIdqbtZNP0TTRp0gR7e3sqVqxI586dGTduHLNmzSIjIwNHR0diY2Nxc3NDo9HQqVMnHB0dcXBwwGAwMGLECIYNG0ZwcDDu7u7s3LmTrl1vFwH38eCpmQAepxWA2WgmYnMEABc3XcSUZ8LG0Yb+O/pzbP4xdny1g7OrzpKXnXdPG7HXEUJQqmEp4k/H021JNyK2RpB+JZ1mnzV7UF25Y/TOet48/yZI/glag2UizLiaQdzxOADcyrsVWt7dQce1NANvLT4CQPtq3gWuu7q60vDZnvw650N+2+bMjGk/cOTIETZu3Mj7778PwPz58+nVq1eBchMnTmTlSsuk+/7771OiRAmio6Ot7oQNBgMjR45UtIDug7JlyxJYYQ/nzzakWkgg35Yvz5g/r+Ja9XkWHP4c0wFBm8BBnNDu5fM1f+Ki1dG+aTOWHjtJXJaZ6QclOUH1ObxmJvb29gwaNIiUlBSklBikgUaNGpGXl4ebmxt79uyhXbt2VsvgGjVq4OHhwYsvvkiZMmVQqVQMHDiQjz/+mIyMDNq1a2f1L3Xs2DEcHR1p1KgRLVu2xNXVtYif3P0jHrZBzoOkTp06Miws7J7KjnMeR3D/YNpNaveAW/Xg+fPlPzmxyOIVUagEHyR+gN7lnzfM8BXh/N7pd/zq+/Hi7y/iUtblnu+VeDaR+c/MJ+1KGp1/60y1l6rdb/MfGOtHrGfv9/+8jds42TD01FCLb/x/sfTgFd7746j1vFVlT95uVYGgks7WtL6z9xN+NY29H7VSjLSKGVu2VkbKXI67foSb5wukf2HZwO30djDLJx0BQGOXQMmmn3Hm90CcAnVMXXGa3lX82RZxmU1Hz+Ll5UW7du1QqVTExcWRcjSFkCsh/FbqN1JTU7ly5QqfffYZrVq1omPHjvj7++Pr60t2dja2traEhobi4+NDUlISwcHB6HQ6hg8fTvXq1Rk4cCA7d1q003r37s3w4cOtigCPA0KIg1LKm3avlRVAMcOYYyR8RTjeNb2p2KkibuXcCgz+YHGt8MK8F1jefzk/VPyBAbsG3JOBWE5qDhve20DKpRQA/uz5J/6t/LEvYf8gunLfHP31KCqNihcXv4jeRU+JqiVw8HIoNG+Xmn6YzGb+9+dxvJ1s2HU+gc2n42hd2ZMJ3YNx0muISMigRkkXZfAvhtSoMYcNR97h++SqGJIvMzI/ffO809jYaWjY5yxXk8ei05XAp7otW9dH4mzIQWcD744uw5k3z/Lue7WpGdyfmjVrkp2dTc3AmtTMtYQyqVixIjY2NmzevJm8vDz8/f0pVaoUmzdv5vfff2f37t0A2NnZUaNGDdatW8fIkSNJSkoiMDCQrKws0tPTsbW15dSpU5QpU6aIntSD5fERit8nj8MegNlkZsWgFeRl5tFmfBuaj2pO9d7VC81bo28NXj34Klo7LfNazmP9iPUknkvk7OqzbP9qO3En4og/HW+JmnUL0mPSbwqTmXkt84H26X5o8XkLzEYzJ5ecxL+l/y0HfwCVStCjbmkujevI3o9bs/GdEDwcdGw6HUeHyTtYd+Iql5OyCfS8dR0KRYe7WwNK1FiPQdiCECQ6WP5XM5INePk7Yets0dax0XnS+a1pZOXmYavVUq93eaT+NHZ2Nlw4H0CdOnUYN24cU6ZMoXnl5vzCL+Tk5DBp0iSOHDnCkSNHaNmyJceOHeP8+fNUr16d2bNn87///Y+QkBDi4+MJDw+nWbNmHD16lF69eqFWq/nmm29o3749jRs3ZvDgwXh5eRXl43pgPBUrgLzsPIw5xmK/Aji/7jzHFxynxis1CGgVcNv83sHeDNw7kC0fb2Hf5H0FxCVbP90KQMuvW9L0o6aFlv/3YN9/R388q3neRw8eLHWH1iXpfBJ7v99LjVdqcHD6QWq/Xpvy7cvftuzmFUvQrZmJS66J7MYDGLIgG4AmgR5MnDiRFSss9hWRkZF06dKFCRMmkJWVxfDhw4mIiMBkMvH3338jpaRLly7Wenft2kVcXNwTIf8tbrR2d+Js0yAisw2UaWpD+oU0Vk49iquPPTkGi/sGe4cK2DqUILhVdQ6cuEi2ZjUAifH2eBgr8/1Hv2Er/JDCRHZ8NNlkU8qpNCqViuDgYEqUKEG7du2YMmUKer2ezMxMzp07x8SJE9m2bRuHDx+mUaNGeHl50aBBA2soybCwMAwGA0IIi63CE8KdBIXXA9sBm/z8S6WUo4QQNbAEgnfAEtO3l5QyrZDyl4B0wAQYr8uhhBBuwGKgbH757lLKh+I/N+mcxbeH1u7eN0wfNuvfXc/eiZYBvNmnd74J61HRg+5/dif2UCxX9l3BNcAVz6qerHtrHaf/Os2Wj7eQdDYJU66Ja8evYTKYCOodhF9dP9KuWP5cXRZ0oWr3qg9V7fNecSplkffvHLuTqB1RXNl7hfeuvVdo3rz4LLJPJpJTUs2UKVPYu3cv0dHRvNyrNy++M43aZVxpFOhBoxEjGDFiBAAdOnSgW7dugCXkX/fu3QsEAQcIDQ0FYP/+/Xz22WfK4P8QcdKoCXK0uBV3qujKa1NCyDFcZveepQCULjWAq1dX8NLrw1jUaSBGo+DkilbYit04GQLAEmOGLEM6266swQMPxr31PcOHD2fnzp00b96csLAwRowYwdmzZ2nbti0///yzVavn66+/xsvLi23bttGxY0e6dOmCn58fs2fP5syZM0gpqVKlCq+99hr29sVDVHo/3MkKwAC0lFJmCCG0wE4hxFpgKvCelHKbEGIA8D5YRXf/poWUMuFfaR8Cm6WU44QQH+af/+/euvHfXBeDeAUVz2VbzMEY6+Bfd1hd3AIL13T5L3xq+eBT6x/r3O5/dif1ciprhq7hyNwjOJVywivIC2OOkdDPQq35VFoVpZuULpaDP4DO3qL2GbXDoqJ6q0k8+0QCifMtboa3XtxHg3I10el0+Pv7k52VyVfPV7pJUyc+Pp6IiAgaNGgAYJUPf/3111Z98RuZP3/+E+UG4HFAqATZ2ZcBqFH9Z3JyrnDy1DsAtGyVxlvDTOSl76Jr42FcSTiPS90UXun2GjO+m4+LuxMBWWUp7VWKjIwMDAYD48aNY/DgweTk5FC+fHkaN27M//73Pz788EPUajV79+5l9uzZ2NjY0LFjR7Zv386AAQOsm8VSSmxtbdFqi+/L5N1wJ0HhJXDd85g2/yOBilhWBgAbgfXcegIojE5A8/zjeUAoD3kCuFG9sDhx3fXCM5OeocFbDR5Yvc6lnOm5sieGdAM2jv8MfimXUog7EUdCeAIl65fEubTzf9RStFTrWY3oA9EcnmVxGdBhWgfAoiqbejkV51JOXP1iCWZDSYRejVAL0tTZ2KX+89N2dnYmKSkJH5+C7isWLVpkdRMMcPz4cUaPHs2ECRPo3r0769ato107i9aY0Whk1apVfP311w+7ywr/Iil5N6DCxaWuNc2Q6kutEm/g36qgU7bX3glBo1NTvokrza6G4HfZm6yUbOtvoEyZMmzYsMGaf8qUKbz88svWyb5ChQr07NkTABcXF65evYpGo6FDhw5UrFgRs9nMp59+anUR/bhzR3sAQgg1cBAIBH6UUu4TQpwAngeWA92AUrcoLoENQggJzJBSzsxP95JSxgJIKWOFEA9N+Gw2mS39KIYTQG5mLpdCL+FXz++BDv43cuPgD+BS1gWXsi5UeLbCLUoUH2wcbWj5RUsO/3IYGycb4o7HYe9pz5pha4jeF03p0lm0aFEajXdJVuv2MnfVUpKi46lkXwZzthGVrcXl72+//Wb193Nd7r9z507c3Nxo1KgRJpMJW1tb2rVrhxCCZ555hr1799KzZ0+mTp2Km5sbDRs2tMqEFR4dWZkXsLUthVptR9jBHkizIHrn/8jNtEOnV9O8dyX8q3uQEpeFRqcGLH5+sk0WWX1OsoHU1FTc3G5eWS9YsID58+dbz6+7AAGsZc6ePcuff/7JxYsXMZvNhISE8MILL+Dn5/eQe/7wuaN1v5TSJKUMBkoC9YQQ1YABwDAhxEHAEbhVmKfGUspaQPv8/HdlZSSEeFUIESaECIuPj7+bov+0//oK4BFvAqddSSNye+Qtr4fNCGOy/2RSI1Np9H6jR9iyxwsHbwdeXPwiDl4ObPrfJn6u87PVJUTSVTOaEsmk5KTzw8yfCA0NZd4Ps9l4fjcZZxOIiorCwcGBDz74gNDQUEJDQ6lcuTL169dHCMGUKVPYvXs327Ztw2QysWvXLsCy6Xf48GEaN24MKOKfosJoTCchMRQ3t8YcOTqQtLTD6OlMbqZlnyA3x8SGWSfR6NR4lPzHQWL9+vU5cvYQJkxERkXi4OBwkwjw7NmzCCEoX/4fpYIaNWpYVULXrl1Ls2bNkFLi6OiIjY0Ntra22NjYWH0IPe7clRaQlDJFCBEKtJNSfge0BRBCVAA63qJMTP53nBDib6AeFtHRNSGET/7bvw8Qd4vyM4GZYDEEu5v2Wut4RCKgnNQcNn+8Gf+W/uyZsMfqvbPR+41o822bAnlTL6ey+vXV2HvZU6NvDSp1fvzDyz1MqnarStVuVUm9nMqUspMwm6FSpXRCJnbBs2VttvYcT90S5dHpdNRp2QBnvSOturdD52HP5MmTrVa//fr1IyIighMnTtCrVy/rP79Wq8XDw4MvvviCkSNH4uPjg4ODA+XLlycnJ4c9e/bw66+/FvFTePrIzU1AyjxiY//GbM5Gn+pPBeehRKkv0cxRw5VcM0ezTfz4+hY6Dq2Oi7cddk464s4aGNBnEBM/GY/DYid+WzHvjiy/x44dy8CBA8nNzaV9+/ZUrlwZsMSIaNCgAVJKWrRoQcWKFR/5s3gY3IkWUAkgL3/wtwVaA98IITzzB3UV8CkWjaB/l7UHVFLK9PzjtsDn+ZdXAK8A4/K/lz+QHhWCND2aCSBicwRh08IIm1bQWnn3+N1kJ2fTblI7dPY6pJTWyaHZp82o98bjY1FY1DiXcqZsFRUXT5gp16oMXu0aIoQgxZCFs40XOWcvo7JzobSLD79+/jMVevwjNw4ODmbKlCl07979pg3er7/+mr59+1rTBw4cyHvvvcfixYvR6/VEREQ80n4qWMjOtmz+m83Z2KSXpOzRMRhyrtDM0TJ0ldSpKKlTsT41j9XTjhUo27hdB9JIpmLbWlYXzsHBwdbrn3/+Of8mICCArVu33pQ+duzYB9WlYsWdiIB8gK1CiGPAAWCjlHIV0FMIcRY4A8QAcwCEEL5CiDX5Zb2waA0dBfYDq6WU6/KvjQPaCCHOAW3yzx8K11cAD9sQ7LrLZoCBewfS+bfOdJ7fGYDDsw4z/5n5xJ+O5+KmiyztsRS9i54qL1Z5qG16EjFi0cBY/WMS6ccs7qEdc5JIy8kgfsZe8q5mkm7IxLfBze6sFyxYcJMo59dff+XEiROMGjUKsGwGCyGsb38KRYPJlMPp0x8BYJNWhrJ7vkTmmAvNmxq1iQnL3mTisuFcjrcYN+5faxG/zt80n+bNm9O8eXP8/f159913AYtvp5CQEOrVq2ddFQDMmDGD+vXr07RpUzZv3vwwu1jk3IkW0DGgZiHpk4HJhaTHAB3yjy8ChcZOk1ImAq3usr33xKMSAaXHWPyMd/ixAyXrl7R666zyYhVOLjnJurfWMb3GdKu6ZvMxzXHwVjYV7xa1MAEQ6BmFQxXLIN/hpxF836I9RtVQTv21F3u9HfalC+rrFybzXb58OQsXLrTEDc7fADx48CDh4eG0a9eO8+fPY29vT4UKFR4r3y9PAimpYRhyrwFQJvyjm657f1CX1HURxO6PYM7BpWzqM509CdcYte5rpny2gIijCai0ap4LfJbXQvsABe0+vvrqK6s2T0hICCdPnqREiRLMmDGDffv2kZOTQ4sWLdi3bx9qtfoR9frR8lRYAj+KCcCUZ+LS1kvoHHU3OVTT2Gio0acGge0CWf36ak7/ZdFXV9s8mT+qh4mUEpPJMlDblymBKl8f27NCBYYMGUK3n95ECMHY1z/j6NGjt5X59urVi0qVKlmNvxYsWEC/fv3o168fAKNHjyYwMFAZ/IsAJ8cg3N2b4+baBLFBj9CpkbmWyd9vbBOLTychOBxzinolq6NTawnxKomNOYeKF+Kp6KLjLxs1tmkW67B/231cH/zz8vKwt7fH19eXc+fOUaVKFbRaLVqtFnt7ey5cuECFCsVfY+5eeComgEehBrr+nfXEn4qnUudK2LrZFprHvoQ9Ly55kWV9l5F8MZmA1rd396BQEGO2kcunDYAgLzmdHT3HUve7fuj9fBj89hBarNyIc9dBeAx8Fo277W1lvrfT5hg9evSD7YDCHaPVOhMU8BPxPx/DSDZ2tT1x7RRYII/KVkNKThpe1Utb0xxt7EnJSaOEgwdanQaRHxns33YfAG+++SbLli2jbdu2ODs7ExgYyJEjR0hLSyM9PZ2jR4+SlJT08DtbRBRP888HzMNUA004k8ClbZesQdrLd/hvPzUqtYouC7owcM9A3MrdvcXv047WTku7MbWxtZecOu/Jlt9zWVJ/HNJkIu9yFObUKOyqO6JxL3wSVni8SF13CWNcNg4hJXF5/uY9HZfnyxH4agOynMy49ayI1zu1yHY0U/mzVpQe1xSdvRZjnmXVUNj+z9SpU4mIiCAhIYF169bh5ubGmDFjeO6553jnnXeoUaMGvr5372n3ceHpmgAe8AogJzWHHyv/yLzm8wBo8UULag2q9UDvoXAz9UY+xwcZo/nU8CnBz5bgUrQLsctDyY20aIwsPnSIRo0a0bhxYw4dOlSg7MSJEwvdELxOSEgIgwYNsp63bt2a5s2bU6dOHRYtWvTwO6dgxZSWS9ZByx5AxrYrZOy8ORyoUAkaNG7Izp070VZxJdaQiKOrE3aeFh9SWjstudl5he7/5OTkAJZA8fb29tjZWWwLunbtyrZt25g8eTJ2dnaULl36pvs+KTwVIqCHpQZ6YcMF67FXdS8avacYcz1K1Do1TcZ34ciq6YSN30Sj/gGkmkz8tGQJe/fvJzo6mj59+lgDeQCMuIUjOIBVq1bh5FQw2MyaNWvQ6XSkpaVRo0YNq5sAhYdL+s5oUtcWVL21CXCxHkuztP4/u7q6MnToUEJCQhBCFLD78LRzJj0u85b7P4mJieTl5dGkSROaN28OQN++fbl8+TJ2dnZMnTr1ofazqHk6JoAHrAZqNll81P/18l8ABPcPptH7jdDon4rHWaxwr+SNr7/g/GED5YIOEGZrQ9OQEHQ6ndW9b8OGDfnxxx+pVeuf1dnnn3/Otm3b+PDDD4mMjKRz586cPn2atm3bMm7cOJo1a8Zzzz1n3UDOzMykQoUKuLq6MnXqVMUq+CFiTMgmdfVFbMq7YjhrcRDsO6YRpjQDcTOOkhvxj9Nhv6+bIFSCAQMGMGDAgAL1BAcHs2j5XPKyUwvd//nzzz8Lvf/TZPCniIDugVWvrbIO/gD13qhHicolHkjdCnfHlYORxMdL0vMcWZsSwx47Dac3rSXmyhWmTJlC7dq1mTRpEsOHDy9QzsXFhffee8/qGsLOzo4uXbowffp0WrRowfbt2wkNDeX06dOEhIQQFBQEYHUNofDwMCZmgwRjfBa60hb3DjGjdnNtwsECgz+AKc1AXnyW5Tgzj+QNlzj/3QGmfjuNoMq1GH3kSyKybnbH8s0339CqVSuaN2/Oli1bgMLtAq5cuULz5s1p2rQpjRs35l5D0hZXnopX1gepBZQSmcLRXy2xZ+sMrUOJKiXwDCo+QVSeNjZ8vIK8DIF9zcvYS4mdTktOXh7zvl9J06ZN2bZtG8HBwVZ3wNf9wVx3AhYfH8+FCxfIzc1l9OjRjBkzxurwrU6dOuzYsYNt27Zx9OhRmjRpwrBhw4qyu08FNuVccGjqR97VTPiPiHYAV8dZlC9sgzzIPm4xxMzJSWfm75NY3mc66zeGMe7oj0yWU62hQNeuXUtqaupNRl6F2QWULFmSJUuW4OnpyalTp3jttdfYsWPHg+5ykfF0rQDuUwvoyLwjTKs6DY1ew/ALw+n4Y0fqDauHWqvo8xcV5Vpb9LNr9mpD24++p7S7KxFJBk4fiuTyqSTs7eyxsbGxugOGggZhixYtolWrVqSkpPDss8+SkpLCqlWrmD59Ohs2bCAhwTKoTJw4EU9PTzSap+KdqUgRGhV5VzaSML47XziP53ePdbctc33wBwhNOE29ktWJKWFHmpsXBgykpv0T/W7JkiXk5OTQqlUr+vTpQ2pqKlC4XYCzszOenp7W60/a3//J6s0tuF8R0JF5R9gwYgPZSRb3sq2/aY1rgBIVqjhQt39TQj/YS9SuSK4lR2On09Gr26vM/X0O2bkZLPtrOUeOHOHcuXNWd8A3bgheXwlc1w6ZM2cOI0eO5O+//8bX15d58+axbNkyIiMjmTRpEuHh4UXW16eFHGMOx1b9ii+g2X2IpXXPcd4mikP2p1Ghwi/Xk4rZZRl6rcdNZU/Wz8Ktug+lAqvR5IMGhEUlot+qJ+piDC41LS8LMTExuLu7s3nzZn744QfGjh3LuHEWTzT/tgu4jslk4o033uCTTz55JM/gUfF0TAD3qQV0fP5xspOyqdqjKi2/bKkM/sWIjZ9YYsLal9Jz+fx2nL2DGDFpFIGujRk7bziNW9Tj6tWrBAQEWMU/1zcEC1MN7N+/P/379yc3N5fOnTuzc+dOVq9ezS+//MK8efMU1xAPmV9P/sr0Y9NJfzGd151f4PP2H2Ovc+BM0hn8nf35ff7vfDtzPOvSt7JzQByLB88hMSOXamVdmTBhAisnryQpKYnIyEjS09Op4NyeTDIZ9Go/tDpBx44dcXNzo127dowdO5ZFixYRFRWFp6cnI0aMYOrUqXz//fd07dqVdevW0aGDJQDRa6+9RseOHWndunURP6EHy1MhAspKtGwSXQ8veFdlE7K4euQq9p72vPj7i7gFuhXLwDJPI8kXkzky8xRqnxTKNA8EaaBC/aZcvZiGIUlN/76Dad68OT179mTSpEkcOXKE8ePHW8sXpho4ceJEWrRowTPPPMOwYcMoUaIE/fr1Y8eOHaxbt47evXvz/vvvK4P/Q2Jv7F60Ki0/tvqRYS+MxcHG0eKYz70y2enZTJkyhSN7D1NhcAsOzl6Lt7sd1cpaXsjeffddQkND2bZtGwCdO3cmy5xCJpkM7PsWu3btYsuWLVSqVIldu3Yxe/ZsPvzwQ3r37s306dNJTEwEbrYLeP/99/Hx8eHNN98smofyEHkqVgA5yRaDDwefu3e8tnLwSrISsvAO9n7QzVK4T+w87NA4gjHeka1f/YEmADwDyhNx+Boyz8SbI4bw6dgRBcrczjXEjXYChaG4hnh4ZBuz2RG9A43Q0KzkzXGj9u3bR9OmTdHpdJQN8OKMwcic/RsYUP+ZAvmMRiP29vZ88MEHJMVlokOHNGoYP348HTt2RKfTsWfPHuLj45k1axYzZ85kx44dDBo0iOTk5AJ2AWFhYUyaNInGjRvTvHlzSpQowR9//PGoHslD56lYAdyrFtDFTRc5s+wMbuXd6Lul78NomsJ9YONkwxsn38ajihuGg/5kbwzi75fWsnng3yQvOkDSiatF3USFu2BntMVgr0elm2X7AElJSbi6Wt72p7f7DpWtmkVhN+vyL1q0iLp1LXEg1BoVEomnqz/vv/8+Li4ubN26lV27duHm5sauXbto1KgRr776KgsWLCAwMBC9Xs++fftITk4mICCggOrvsmXLSE5OftBdLzKeihWA2WhGpVFZ1cDulBOLT6B31TPk+BA0Nk/Fo3rscC7lzNAjb7Gk/0Ji9iaSl2zI1/ZS8WurX6k7tC4VO1UkoHWA9e+fHpOOg7eDIsorZmhVFs+uzwU8V+h1Nzc3UlJSAPBycEWTriPwSk2+/nwXxjwzJqMZtYRZC6djayc5fuwoqxfvpnvfNmSlWKQAqamp2Nvb06lTJ3bv3s3p06etcX5PnjxJ9+7drZ5hrxMaGgrA/v37+eyzz6yT0JPAUzGqXZ8A7oSTS06ytMdSvIO9uXrkKuWeKacM/sUcIQQ95lpk+VJKhBAY0gxs+mgTYT+FceDHA+gcdQghcCrlRPzJeKq9VI2ui7oWccsVbsTH3hInY2PkRqp6VL3pev369fn000/Jy8sjNjYWN7yonFyevFQDOhVIteBqUhSGtAzKONdh1DvbsDEa0aAhbP9uetOItWvXEhISwsyZM0lJSeHLL79kzJgx2NjYsH37dvR6PV9//TUhISE3RY17EuNCPx0iIKP5jmwA1ry5hqU9lgJw9ahFfBD0ctBDbZvCg+X6W76Nkw0df+zI+wnv89zPz1lfArS2WpxLO3Mp9FLRNlThJiq6VaSsU1m2XdlW6PUbff70eKkHLzR+lUt2+zH7H+S9H1ry/uQW2PtfoGaj+uTY2mMWYGcAN9xYtfkXGjduTNOmTRkyZAgXLlzgtddeY/r06ZQvX57GjRtz7tw5WrZsydatWzl16hTr1v1jf2A0Glm1ahUvvPDCI3oajwgp5WPzqV27trwX1r61Vo51HvufedJj0+VoRsvRjJZGg1GaTWaZFp12T/dTKN5s+GCD/FzzucxKyirqpij8i1G7RsngecEyMTtRzpkzRzZs2FA2atRIHjx4sEC+ga8PkYE+NWTV6tVl2bJl5YgRI6SUUmZmZspnnnlGlixZUoaEhMiYyFjphZdsWq6F9Pb2lgMHDixQz+rVq+Xw4cOllFJ6e3vLvLw8KaWUP//8s/zmm28K5Hv55ZcfZtcfKkCYLGRMfSpWAAD8h0X5wZkHWfjsQgDaTmyLWqdGqASOvo6PqHEKj5LqvaojzZLNHz3Z8V4fR54r9xxGaST0bChTpkwhNDSU+fPn3+TLqXL5Jgzt/DVh+/dTuXJlq1fXMWPGMHjwYLy8vNi4cSO50oAOHV0q9rC6876+jwCwZcsWKlasCGDV+gEICwsjMPCf4DNPovgH7mAPQAihB7YDNvn5l0opRwkhagDTAQfgEtBLSpn2r7KlgF8Bb8AMzJSWWMIIIUYDg4H4/OwfSynX8BAQamG1Bv43ywcs58icI7iUdaHDtA7UHVL3YTRBoRjhVd2L+m/VZ+/3e/Gr70dwv+C7VhBQeDhUL1EdB7Wev9Ytsqp8+vv7F/DlZDaZyY1RobbJZuaMGYSGhhJxOYpXRrzM5s2bycvLIz09nYCAANRqNSbMfL/jG6qrquLl5cX48ePZtGkTZ86cwcvLi5MnTwIQFBTEM89YVEobNWpkFfdkZGSwZ8+eJ9JL6J2sAAxASyllDSAYaCeEaADMAj6UUgYBfwPvF1LWCLwrpawMNACGCSGq3HD9eyllcP7noQz+17GsggoStSuKI3OOAPBG+BvK4P8U0WxkM3zr+rJiwAoWv7CY2MOxhf5GFB4t2isHaZyayLaLe3FwtLem3+jL6a8tG3DK8CQ7J5PPxoymet3GvBT0ET9/vIijh49SM6g6Z86coWzZstSvX5/X7YcxxPcDa/Cfr776ipEjR9KsWTOaNWuGVqslLi6OpUuXkpCQwJUrV4iPj7f+HhwcHIiIiHji/ADBHUwA+SKk64FTtfkfCVTEsjIA2AjcpFIhpYyVUh7KP04HTgN+D6Ddd4UQ4iYRkCnXxO+dfgdgRPQI1DrFodvThK2rLQP3DKTthLacX3+embVm8rnqcwzphqJu2tPNod9IUKtR26sJPWVx83Eq4RRhkWG0Xt2aA1cPEJ9hcfwWHXsRczY849sHFxd3cnOTsLVxJHO/M8s+mUdaTBxHdx1jruEXFkfPtsZ/NpvN/PjjjwU8u166dMkaDN7R0dEaDP5J5472AIQQaiHEESAO2Cil3AecAJ7Pz9INKHWbOsoCNYF9NyS/IYQ4JoSYLYQoVLlWCPGqECJMCBEWHx9fWJY76EDBFYDZaGas41iyE7OpN7yeIut/SlGpVTQc0ZC3It6i5dctAVgzbM0txYUKD5ndU+HIfFLUKuzK2XHhaCRN5jeh86+dQQcqrYoB6wfw15UlSMzEX72KSqUmtYIHdV/zpkyVAALLeXEm8SwxSaWJupaMVuPKQNfXKW/rwcKFln2+efPm0aVLF/R6vfXWNwaDj46OfuKDwV/njtY0UkoTECyEcAH+FkJUAwYAU4QQnwErgNxblRdCOAB/Am/fsE/wE/AFlnfzL4AJ+XX++94zgZkAderUuaf/zH+vAMJXhGPKNeFVw4t2k9rdS5UKTxCOPo40/agp5jwzoaNC0bvoaT+lfVE36+lj5/cAfFhtMIPPzkPT3IFDYw4hEPj08sF42YjDliQq1DKzvvwkIrdfRF/CxLuD3Rm1eTCHI4/jO6wUs377krLZbtjbqfh2yofEf7SdjVeiObR6F/b29vz1119ERERw+PBh661vDAbv5eX1xAeDv85daQFJKVOAUKCdlPKMlLKtlLI2sAgodL0khNBiGfwXSCn/uqGua1JKk5TSDPwMPDzvWjfs7107do0lXZcA0HVRV2XzT8FKs5HNKNWoFAd+PIDZaC7q5jx9uPoDUK9CV1pkZuHR1IV2459h566dHPzoIKc/Pc30mX/g3/oF3u72DhPnjMfbpST91w7iaHwq7na26L311Hy3DMfPXmDkF2M4euoUOjsVAeqSvPLKK2zevJmAgAB69OjBBx98wPr165k1axbwdAWDv86daAGVAPKklClCCFugNfCNEMJTShknhFABn2LRCPp3WQH8ApyWUk781zUfKWVs/mlnLCKlh4Yxx8iyV5ZZo3n1WNZDCeOoUID4k/Fc2XeFai9Vu2PLcYUHSNxpAFRTgxlvtmNF8gji8irjUd8HZxuLb/7agc2oHWhxFGcuacK1ztvs/Saa8g5+/DV/IxIz69auYN5v85kzZw4XL17EKVuNJseG0I9/wdvbmzfeeIMxY8bg7OyM0WikfXvLaq9cuXLExcWh0+kKeHudMWMGs2fPRqfTMXr0aFq1avWIH8zD405EQD7APCGEGsuKYYmUcpUQ4i0hxPVdlL+AOQBCCF9glpSyA9AY6AMcz99DgH/UPb8VQgRjEc5cAl57MF26GTt3i1vXo78eRaVRMfTkUNwruD+s2yk8phz65RAqjYp2UxSxYJEwdA9Mrg7A8TI/cjlFhw3wx7gDjJh8s3+grdtGkdBEz4znX6LXc7Ot6YHlytOqVSv27t1LdHQ0HWs3o7/5Jby9//HoO3DgQD799NMC9TVt2pRBgwbRpEkTa1pcXBwzZsxg37595OTk0KJFC/bt24da/WQojdx2ApBSHsOyefvv9MnA5ELSY4AO+cc7KSCAKZCvz9029l6p/1Z91DZqcjNyafpxU0Xso3ATZqOZ2LBYXMq6WF8YFB4xrmX4Lf4nVBhp2acVw/rqmfXmLmwM9py9dIkKZcsCMHfuXGbOnMnVpHBUPV1p+8xb1iomTpzI3LlzSUpKomLFinTp0gWDzGNu1nL+brQdk8lEUFAQ27dvZ926dZw4ccLqIjwtLY0RI0ZgY2PDG2+8QY8ePQpoB2m1Wqt2UIUKFYrgAT14njzF1kLQ2Gho8FaDom6GQjHl7KqzrB+xnqRzSdR/q35RN+epRusTSOKVDP4af5DnhweT6hWD8zVfNo67yBrVadJNScz4+ws+GPMlV1M2MnX2HxxssYx23jUASzwHb29vzp49y/79++nWrRubF/5NbRoxc+P35Kl1VKlShePHj6PX63F1dWXgwIH06dOHhIQEPDw8SEpKolWrVtStW7eAdlB6evoTpx2kCDoVnmriT8fz+wu/o9aq6bGsB898/8ztCyk8NNq9Wg0vfycAsjNyGfZeJ/TNU8gsE4PWbMPVy5fx962M+qgXfpG9sUu3o6p/QfGQm5sbMTExRERE0KBBA7KMBnzxJTchGa1Wi0ajQaPRoFarsbe356OPPqJdu3ZcuXLFWr5NmzYcPXq0gHbQO++888RpBz0VKwAFhVsR9lMYaq2aV0Jfwb6E/e0LKDxUXDztaNGnEr9/vp+Ns09RpYkvle2q0+ijQK4lJjLr5zA8smvjVjmFpFAXSrm4odH4FKijfv36vPrqq/Tt25eoqCjsbG3RoCE3JZXvfv2Vzp07W+NDd+7cmWrVqtG4cWNeeuklTp8+TW5uLrt27eKVV14BLNpBXbt2JTY2lgEDBjxR2kHKBKDwVBOxOYKyLcoqg38xIiPZgCFtISqND6d2tgDA3sWGGi1LUTu4OuvWreOZmtVYFHqF9Mws3NzcCpR3dXVFo9GwevVqtm7dykdd+7B1UiyvvP02Np4lKFeuHPXr10ej0VCxYkUGDx6MVqslJiaG+vXrI6Wkd+/eVK1qiUnQt29fLl++jJ2dHVOnTn3kz+NhokwACk8t2UnZJIQnULFTxaJuisINRBxNQKjdECona9rOJeeo0bKUNSjMXscqJKU7Ym9ra32bv87Zs2fx9PRk7969AJyZtZRlnCE5JYWN69eh0+kAMBgMSCnRarVER0fj5eXF3r17b1ISeRKdwF1HmQAUnlqyErOQJqmoBBczTm6PRmffjvavB+FRyoHfPtljvXY9KMw7n45FJeC35Ys4cuQIGzdu5P33Lf4o58+fT6+XexK1/GvCI2PZc9iFP/mTckmlreEeFyxYgNlsplOnTtjb22MymZgxY8ZTpyGoTAAKTy16F4svmJz8eLEKRU9ujtF6HBBsMdQcPKlZgYF5wIABaA874O2eQZ06dQCsqpwAn3/+OaFzPmf2YTMqXDh66SheeKFS2zBx4kRq1aplzTt8+HDGjBlDmTJlGDNmDBUqVMDPz49+/fpx9OhRnJ2dKVGiBH/88Ye1TFJSEuXKlWPq1KmPfYwAZQJQeGqx87DDuYwzu7/bja2bLUG9glCpFcW4okSj/ef5ZyTn4OCqR6e/eZgymjUU5r3blJvNhl++ZN81LcG6SBq89B6tfv+LfvSjdOfSDB8+nJ07dxYoU5hRGMDUqVMLGIVdZ+zYsTRu3Pgeelf8UH7tCk8tQgi6LemGg7cDy15Zxpwmc5TVQBGjUqvo+r/aAMz7aDeJ0RmF5vNyTefctdKcnLfQmpaXncHC7z9h3zUt9bxMPP/eTxw5e5mGDRqiQYOr2tUaWOZGfv31V5o0acLIkSMxm//xATVixAiaNm3K4sWLrWlRUVHExsZaVx6PO8oEoPBU41fPj8H7B9NpbidiwmLY/IkSJrKo8fZ3ptlLFkvbJWMPsGPxWS4ejicj+Z+Bu81H3SjjFk3oHm9OL7AM0GdCl3Ah24mQAD0dhnyBSqcnKSmJEl4WUZIhzVAgsAxAp06dOH36NNu2bSMyMpIFCxYA8N1337F//36WL1/OuHHjuHjxImAJOfnJJ588kufwKFAmAIWnHqESBL8STNDLQRyff5ysxKyibtJTT1DzkvT5qiH+1T04sT2atTOOc3DdJet1rb0d7T97CXd9LFt3uJF0cA8nT57CjhxCXn7Xms/NzY2snCykkBjSDaSmphZQG3V1dUWtVqNWq3nppZesMYE9PDys5a8bhR0/fhwhBJUrV340D+ERoEwACgr5BA8IxpBm4Oyqs0XdFAXAyd2Wdq8G0f8bixz+WkQap3fHci7sGuF7YzmxO470PDc0Ihf0es5k2BPoCiqN1lpH/fr12bVrF2admWvx13BwcCigNnqrAPHX068bhVWoUIGDBw8SHh5Ou3btmD9/PuPHj2f//v0P/0E8RJRNYAWFfMo0LYNrgCtbPt5CYngilTpXwre2L0L1dKkGFjds7DQ4uNoQH5XOll9PF7gm0NHKeRLZS84iZF+83PUFrl9XGx396mg0+zUs2bSkgNro9QDx143Cxo4dC0CPHj3IyMggLy/PahRWtWpV+vXrB8Do0aMJDAws4Db6cUQ8ToGw69SpI68v0RQUHgZX9l1hw7sbiN4XjdloRuegw6eWD86lnZFSIs0SG2cbGr/fGNeAQqOYKjwEcjLzSEvIRq1VYcw1Y2OnsXzUeYiTfzD9F2/MUke1NimEdO1yU/kvvL/Awd+Bd/a8UwStL3qEEAellDftXCsrAAWFGyhZvyQDdg4gOymbs6vPEr0/mpgDMUTtjEKoBUIIUi6lcPiXw7T+pjXVelRTYko/AvT2WvT22kKu6NhzpSlmGQnA8d1a6ncwoLctaB2s0qswZhkLKf90o0wACgqFYOtmS40+NajRp8ZN1xLPJrLkxSVsGLGBDSM20H9Hf0o3eXIchD1OGLLyOLr5MqWruuFYEk6uhw2/H+D5/gX1981STU6CMgH8G2UTWEHhLnGv4M6QY0MYsHsAehc9v7X9jT3f7yEvK6+om/bUEXcpHVOemQr1vGneORipMnH5UCYmk0Wff+7cuVStWJ1Z12YSm3EVk8lUoPw333xDq1ataN68OVu2bAGge/fuNGrUiPr16zN37lwArly5QvPmzWnatCmNGzfmSRFFK3sACgr3QWZcJn++/CcRmyNw8Hag9/reeFX3KupmPTVsnnuKs/uvMXBiU3R6DWvm7yJipwGpyUPlksEPSz/j1abfEbMpjMVRvxBpumQtu3btWnbs2MHXX39doM5z585Rvnx5cnJyqFatGidOnMBgMGAwGPD09OTUqVO89tpr7Nix4xH39t651R7AbVcAQgi9EGK/EOKoEOKkEGJMfnoNIcQeIcRxIcRKIYTTLcq3E0KECyHOCyE+vCHdTQixUQhxLv9b2VFTeOyw97Sn76a+9N/Rn4yrGYRNV15QHhWRX07gzJ4YzGaJOt+FRJse9fEKFmgcjJw8dA4vbUW0ahu83XzIlYYCVsBLliwhJyeHVq1a0adPH1JTUwEoX748AFqtFpVKhRACZ2dnPD09AdDpdGg0T4b0/E5EQAagpZSyBhAMtBNCNABmAR9KKYOAv4H3/10wP5D8j0B7oArQUwhRJf/yh8BmKWV5YHP+uYLCY4kpzyJa8AzyLOKWPB3knDlDxoLZeF2zTLi7xy1j7ty5hIQ04/vfPqVedx+kayp2jnp8a6txLelEjswpIO6JiYkhMjKStLQ0du/eTaNGjTAa/9knGDlyJFFRUQUcwZlMJt54440nxhr4thOAtHDdIYc2/yOBisD2/PSNQNdCitcDzkspL0opc4HfgU751zoB8/KP5wEv3EsHFBSKAxc3WVwF1Oh786bx7Yg/HU/G1cJ93ijcjDSbieo/AJU00+HzDmjzMjhwJo9JE78nNDSU+fPnM3z4cJ7vE0L5ep50HhzC6bQT5JHH+tXrCQ0NpWXLlri5uXH8+HGWLl3K+vXrSU1NZePGjYDFP9Bff/1Fq1atCtz7tddeo2PHjrRu3boouv7AuaNNYCGEWghxBIgDNkop9wEngOfzs3QDShVS1A+4fMP5lfw0AC8pZSxA/nehr05CiFeFEGFCiLD4+Pg7aa6CwiPHs6rl53sp9NJdlYs9FMu0KtOYEjhFcUFxh8RPnYopORnP99/Drnp1alaVXEi4gJfJl7zkLPz9/cnIyCA4OJidO3eSl5fHzqitqFDRoWMHq7inefPmODg4kJKSwoEDB7CxsaFEiRIsX76c2bNnU7NmTerWrWu97/vvv4+Pjw9vvvlmEfb+wXJHE4CU0iSlDAZKAvWEENWAAcAwIcRBwBHILaRoYSaUd7XrLKWcKaWsI6WsU6JEibspqqDwyKjctTI6Rx2Lnl3EhY0X7qhMZlwmv7/wOwB5mXks7bEUs9F8m1JPN+bMTJJmzwGVCrcBAwCoO6ITpUuno7b1JHTsKgCcnS2Ge0OHDiUkJIRj0YcpS1k+efMz0tPTGTt2LP369cPd3Z169eoxaNAgqlatSp06dejVqxcnTpzgwoULzJ07l6SkJMLCwpg0aRI7duygefPmdOvWrSgfwwPjrtRApZQpQCjQTkp5RkrZVkpZG1gEFParv0LBlUFJICb/+JoQwgcg/zvu7pquoFB80NhoaPllSwDmt53P+hHrOfH7Cc6tOUfk9khyMwu+H5lyTSx5cQlZ8Vm8evBVnp/9PBGbI9j4v41F0fzHhrjZvyANBux6dC0QJKb6C83Iy0niUo4vMbtOWJ2+DRgwgN27d1OvYkOCCaZsiXJ89913HDt2DBsbGyIiIjh//jyZmZn4+vryxx9/sGfPHl544QX2799Pv379cHNzo06dOuTl5REaGkpoaGiBfYHHmdtuZQshSgB5UsoUIYQt0Br4RgjhKaWME0KogE+B6YUUPwCUF0L4A9HAS8DL+ddWAK8A4/K/l993bxQUipB6b9bDzsOOXd/uYu/3ewtc09hq6LqwK5VeqATA2rfWErUjii4Lu+BTywefWj5cPXyVvRP34lPTh+q9qxdFF4o9J87vwg/IWvQHB4J8qdvldQAaNGhAnOk9TCYjv/+w4yanb7Wr1uXwsf1kJWVzJu4ogYGBAKjValxdLQqIJUqUICkpqYDTt/Pnz2Nvb0+FChUee78/hSKl/M8PUB04DBzDIvf/LD/9LeBs/mcc/9gU+AJrbijfIT/PBeCTG9LdsWj/nMv/drtdW2rXri0VFB4HUi+nyst7LsvLey/LE0tOyJ+q/yS/svtKZqdkywM/HZCjGS03/m9jgTLGXKOcEzJHfqn/UkaHRRdRy4s343Z9JXuMriK31K8kT1SqJHMN2dZrv/zyi/T3qiIDvKrKAwcOyMOHD8tvv/1WSinlnt8Oy+pUl7Uq1JFt2rSRsbGxUkoplyxZIuvWrSubNm0qn3vuOZmenl7gfqNGjZK//fbbo+vgQwIIk4WMqYohmILCI+DKviv80uAXyncsz4X1FwhoE0DPlT1vCkGZGZ/Jz3V+RkrJq2GvYu9pX0QtLn5k5WVRf2F9ACYmP0PJ6asxzxpH1SadrHl+fN1izTtsessCZc9sjGBx219p+k1bWn7Q8NE1uphwz4ZgCgoK949fPT8c/Rw5t/ocLv4udF3YtdD4w/Yl7Onxdw+y4rP4o9sfVvsCBTib/E+cBs/qFnGMatCHpCddY+7cuTSoV5+Jy4YTf/VQgXLSbGbajC+Zxzze/uE1q8uHtWvXUrduXZo2bUqvXr2sNgCtW7emefPm1KlTh0WLFj2i3hUNygSgoPAIEELQe11vyncoT88VPdG76G+Z16eWD8/Neo7I7ZGsH7H+EbayeOOqt8jqG/k2okbL7pyt6w3A/pXzmTJlCivnLKRvy49YuGeWtUza2dNMGPg2Z8IzeYVXGN92EC1bWlYHI0eOZOnSpezYsQOtVmu1AVizZg2hoaFs2bKFjz/++BH38tHyZNgzKyg8BnhW8+Tl1S/fPiNQvVd1rh6+yp4Je/Cp5UPN/jUfcuuKPyVsLWrg1yeC8kPfg/7vceHTSZSVOYQfW4GHUw2yTEb+GPEzcVnlAFi6+ywlXY3MYx67tznTIPUVnJ2dqVq1KikpKZQuXZrU1FSuq5nrdDoAMjMzqVq1ahH09NGhrAAUFIoprce1JqB1AKtfX82VfVeKujlFjq3GFoD1EZZVkZ2bJ6n2gmtqiU+OJO+ng5Z8OnsuxNtjp0qiVsmD2LokkuGsxkgex2Iu8dZbbwHQt29f2rVrh6+vLxs2bOC9996jefPmREVFERISQuXKlTl9+jTNmjVj/Pjx1nYMGDAAHx8fBg0a9IifwINHmQAUFIopKo2Krr93xdHPkSVdljz17iJkvg2pSVr2RcpWrEuDg6eoO30Wqc3rY5dt8RSQk5vIa+Na0H/aizT89H3cSpbm/IULvKZ/meFBHVixYgVgceuwf/9+xo4dS9WqVRk2bBihoaGULl2abdu24e/vT15eHitXriQ0NJSzZy17EF988cUTszegTAAKCsUYO3c7Wn/TmvSYdMJmPN0acCphGa7kv5wJ1K9fnzOXrmE/byhTyg9F45GNq5+P9bqfnx9eXl7Y20JCWhpqtRqDwVDABuDChQt89NFHfPzxx9bN4PT0dBwcHNDr9dSpU4fQ0FBrfU8KygSgoFCMyU7OZvNHm3HwcaD2q7WLujl3RHZyNisGr2Ba1Wns+PrB+cxPzE60HiflJFmPrwd+f77LIK5Mv8znn73JkSNHrGKb2rVrk5GRwbTMRWyMPk5gYCBJSUl8+eWXtGzZkpkzZ9KoUSMOHjzI2bNnqVatGi1atODatWv07dsXIQSbNm0iKSnppjY97iibwAoKxRRplvzd+29So1LpF9oPR5/iH3s442oGv7X9jYQzCZjzzGz5ZAs+tXwIbBd433U76v7pf0J2Am56N+v5gAEDiHNezdK0cLp1egO9vQfBwcEAeHl50aJFC4IyNOj0ZiZkb8HNzY1u3brd5NNnwIABrF+/nsmTJ3Py5Eneffddtm7dSkBAAL6+vvfdh+KGsgJQUCimhI4O5dyac7Sb3I5SjQpztlu8SLmUwuwms0m+mEzV7v9oz6weuvqBhMvUqXV8VO8jAL7a+xV55oJ17ks7TyU06O09CqTXr1+fnTt3orI1E5OedpObiJSUFOvxli1bqFixIgBVq1Zl3bp1rFy5kqSkJNq3b3/ffShuKBOAgkIx5MzyM2z/YjvB/YOp8/pNBpzFjuzkbOa1mEd2Ujatxrbi1B+nrNdSIlIIXxH+QO7Ts1JPanvV5lDcIU4knLCmb9rzHedVZmo5BtxU5rqIaFzkYmZeXcukSZMKiIjGjx9P/fr1ady4MUlJSQwePBiAiRMn0qJFC5555hmGDRtmVRP99NNPeffdd1m7di2tW7cmMzPzgfStKFBcQSgoFDMSwhP4ue7PuFdwZ8DOAWj0xV9Su3b4WsJ+CuOV0FdY1ncZyReTC1zvs7EPAa1vHpzvhWGbh7H9ynZG1B5B/2r9uXJlLx02DUIKwdzg96hd45VCyy1r9RGXDpl4O/nbB9KOxwnFFYSCwmOAId3A4s6L0dho6PFXj8di8M9OzubwL4ep8mIVDvx4gOSLyXT/szvuFdwBCHo56IEN/gDVPSyeUo/FHwPg8rXDSCF4zTmI2jVe4eq6H7g2phrpUWeYO3cujRo1onHjxkTlJWAwqAvU9c033xQIEwkwbdo0KlSoYPUYep0nSf//OsX/16Wg8JQgpWR5v+UkhifSZ2MfnEs7F3WT7og9E/aQl5WH1l7L4V8OU+G5ClR8viJu5d1Ij06nXNtyD/R+fav2ZVfMLjZFbWLhqYWcPnscgM3xBxk2yhnv/DAB4ZPbM3mTA/sOHCQ6Oprn6jWnh+EVpMmEUKtZu3YtqampbN68uUD9Xbt2ZfDgwVSuXLlA+hdffEHfvn2ZP3/+A+1PUaKsABQUigk7x+3k9F+naf1ta/xb+hd1c26JKc9EXnYe0izZN2UfO77eQfU+1Yk/GY93TW96ruiJSqPCK8iLwHaBCFVhgQHvHVuNLd83/x6AsQfGsix3B545KnqnZJJqcgAg1r8XEdfiqGWfSObJLfj7+2OQeeSaJab0VACWLFlCTo4lUPz1MJFg0RrSarU33fdJ0v+/jrICUFAoBlzYcIEtn2yh2kvVaDii+LkrjtwRyYoBK8iMy8SQZgBA56gjNz2Xip0q0vGnjnzn+R21Xq31SNqTl3SCFnl2bNVm0UxW58fXFgBgykknKzMDH3cflm7aRyndCVyX9yByZ3vs1CqyySY94jyuNesRExODu7s7mzdv5ocffmDs2LGMGzfukbS/uKBMAAoKRUxyRDJLX1qKZzVPnpv1XIFQh8WF03+dJvVyKrVfq42dux1CLUi7nEbpJqWp1rMaCacTyMvKo0SVBxu3e+7cucycORMhBFOnTqVWrVpERPzAxYjvyTuYgnEDnCzljv8Yf7p06cKECRM4evgE777blfT0XBy0FRikcUAdsZpziVkks4TNL/7Bz4v/xM3NjXbt2gHQrl07hg8f/kDb/jigTAAKCkXMpv9tQpolPf7ugc5eV9TNKZRrR67hHexN+8kWXfhLoZc4+utRks4nEbElgkM/H0Jrp6XicxUf2D2Tk5OZMmUKe/fuJTo6mj59+hAaupaIS1PwcG9Jp/Z7eP4ZLfUbzaR3v7etRl3Dhw/nzz//xNHRkbJly5L6wi7iVk/A3/EPuqcMpOabfrzzzjv07t2bsLAwWrduTVhY2E2bvk8DygSgoFDEpEen41vHF7dybrfPXARIKbl65CpVe1iMu64du8aC9gtAgMlgQpotquQ1XqmBg7fDPd8nLi2H3/ZGMnXLecY8XxXT5cM0btIEnU6Hr6898fGn2L69DRJJ2bJDyYixY/nWRbz9vyCiolR8+eWXAKSmplK6dGnAIrevU7cuOp2O1zoOIHZRLNsXbubomaOYzWZiYmJYu3YtNjY2fPHFFzz33HNERESQmJhIamoqrVu35vPPP6dRo0Z8+umnrF27lqtXr9K6dWuWL1+Ovf3jHbHtToLC64HtgE1+/qVSylFCiGAsgeD1gBEYKqXc/6+yFYHFNyQFYIkpPEkIMRoYDMTnX/tYSrnm/rqjoPD4kZuZi52HXVE345akRqaSk5KDd01LAJYD0w6g0qgYfnE4GhsNV/Zd4eqRq9ToW+O+7tNu8g6SMnMBGLXiJJmnduMtszAYTSQkbEavzyYhMZZmTafi7FyTSnVL8sobv9CmrQNZaY0YPnw4O3fuxMPDg6NHjxIZGUlaWhqjRo3iww8/5OjSPfy1aB2/Hf+NmbNm0qtXL/r160ePHj1o3749HTp04JdffsHHx6fQ9n355ZfWSeZJ4U60gAxASyllDSAYaCeEaAB8C4yRUgYDn+WfF0BKGS6lDM7PUxvIAv6+Icv3168rg7/C00peZh5au5u1TooLsYdjAfAOtkwA2YnZOPo6Yl/CHhsnG8q1KUfj9xvj4HXvb/8ApvyVxOsh5ehc04+OdcoTGRvPsLl/cPbcV2Rn63j+uTN4ez8PwIGwgzSq14TQ0Exe6taUjIwMDAYDP//8Mx988AGD+w9EZ4BffphO905dmP/9L3zHd5hNZtRqNVJKayCY7777ji1btlCjRg1cXFyYOXMmAFlZWQwaNMhqK5CcbDFwe1JsAm47AeQHlb/uiFyb/5H5H6f8dGcg5jZVtQIuSCkj77GtCgpPJNd16IsrV49cRagEXkFeAHhU8SDpfBJZCVkP9D6V853dPVfDh+97BPP9m90Q106xKdyGpLTKeHhUQq+3teZPSkpCo3UCAW5OKTg7O5OUlETVqlX5ru0wKjiWwZhnZmmXqQSmefHD/vnUohYftHubvn37UqlSJbRaLXXq1LH6Azp8+DBRUVFMmTKF5ORkxowZQ/fu3dm8eTOhoaFW99FPSkyAO7IDEEKohRBHgDhgo5RyH/A2MF4IcRn4DvjoNtW8BPz7ib0hhDgmhJgthHC9xb1fFUKECSHC4uPjC8uioPBYk5dVvFcAVw9fxaOSh7WNFZ+riDRLzq4+e5uSFvHWndK1VkkAHG20bD0Tx097rqIN6sDVhR8z9ovTTJnyYwEfPm5ubhw/dZEWzZxIyviLpMQ4rv15gNHDPmDA5I84HX+BXr16oKtmQ0JuPDlGA5e5zLg1E9Hr9Rw7dgw3Nzf++OMP7O3tEULQo0cPxo8fT40aNTh37hybN29m3bp1NG/enFGjRlnb+qTYBNzRBCClNOWLcUoC9YQQ1YAhwDtSylLAO8AvtyovhNABzwN/3JD8E1AOi1gpFphwi3vPlFLWkVLWue6MSUHhiUJYNlqLK1ePXLWKf8AStN65tDMrB61k4bMLiT4QDUDiuUQubbsEWPqzuPNixjqM5eQfJ297j7lz5zJ6cBeuzn+fhWtC6T/3ADO3X8Shelu8+3xH/aa1+d///sfbb79N7dqWuAhCCE6fOcP+fW78uugaUkbiHu3IIMfnmN35WzJzs7gQH03lV9tiU8uHunXq0p/+eNp7UL16dZKSkihRooTVAVzlypVZtWoVERER7Nq1izJlynD8+HFatmzJ1q1bOXXqFOvWrXvwD7gIuSstICllihAiFGgHvAK8lX/pD2DWfxRtDxySUl67oS7rsRDiZ2DV3bRFQeFJQQiBNBXPCSAvK4+0y2l4VPnHxbJQCfpu7kvYjDAO/3KY2Y1m0+PvHizpugRTrom3o94mfEU4Z5adASB6XzRVu906uPp1dc+Zv6+h+/ermfPdKGg/mkH10nE1z+XIEUcc8Wfz5nkFyn366aeMGjWKv//+m1WrzjHiXQ+2Z83j9xVRHIg+BirB6tWrcXZ2xsfHh6ZNm3L04BHSDRns3r0bPz8/nJ2dqVatGnZ2dmRmZlKiRAmklNjb21OhQgVMJhPjx4/nyy+/ZN++fYSFhdG0aVN+/fXXh/PAHzG3XQEIIUoIIVzyj22B1sAZLDL/kPxsLYFz/1FNT/4l/hFC3LjV3hk4gYLCU0JKZArr3lnH3sl7yU7KxsXfpaibBEBOSg6hY0IJHRPK7u92s2v8LoCbVFTdAt1oO74tz818DrPRzI6vd2DKtcTqXTNsDUfmHLHmLdmwZKH3is7J5c3TkXy7dhO5VWrw7aHLaF28iU9ORRrzeLZmMNW9U4k5vKNQlw2pqam8//777N69mxefL0Vampkmr75OeFYEQWXL4+DkiJ+fH0eOHMHFxYXDhw8jNIIQ7yaUK1eOzz//nOTkZHbs2MHEiRPZsGEDeXl5VKpUiZ49e+Lm5kaXLl345ptvUKlU+Pj4MGHChCdm8Ic7WwH4APOEEGosE8YSKeUqIUQKMFkIoQFygFcBhBC+wCwpZYf8czugDfDav+r9Nl+VVAKXCrmuoPDE8udLf3Jl7xUAnMs4F5twjxc2XGDb6G0FEwUFREA3khplGYwTzyZStkVZ7D3tCV8ejjHHiK27LdmJ2SSdLxhKUUpJmtFE7T2WmAHZp85jUtuQkp6NBsjChs4BdgT7VyLZ5XuuxbXEr+TZm1w2eHh4EBa2k+rV6xG2P52gcq7M/2IOUgVdh/Ylbe5crl69iq+vLzk5OQwZMoRPhnxMWlQ63iW9GT9+PLNmzaJPnz4YjUaeffZZHB0dSU9Pp0yZMrRq1YqRI0fSu3dvDh48iJOTE1OmTEFKyeHDh58Im4DbTgBSymNAzULSd2JR7fx3egzQ4YbzLMC9kHx97raxCgpPAmaj2Tr4O410YvGGxfzR4Q+rq4Pr7g9iY2Nxd3fHwcGByMhIunTpgp+fH+PGjcNgMJCTk0OLFi1Yt24da9eupUePHggh0Gg0TJ48md69ezNgwADWrl1Lx44dmTXrv6S0Fq6/xQ87MwxHX0fysvJQqVW3tFO47hfIpawLOck59N3cl1N/nGJpj6XYe9rjW9uXbWO2EdAqAN86vlzOyaXunlMF6lA5OtHUBsLKO2EsZY/LMjPTBlqEC87OdXByVBEYeJTz57+hXbuuVpcNX3z5Au91fxcpzZTVVyXAWI59R3aTlZrErl9/IS8lC1tbW5KSknB2dub5558nZVQ8Xj4leHZjV9555x1at27N5s2b6dixI/PmzSM3N5f69etz4MAB3nzzTcqUKcPXX39NSEgIu3btwtvbm0aNGrFjx44nwiZA8QaqoPAISY5IZkJpi8mMykvFb6t+Y9v2bcyfP5/hw4db5eEDBgzAzc2Ns2fPMnHiRCpXrky3bt0YMWIEAwcOpGbNmtjb27Np0ybeeustRo4cScuWLalZsyYajYa+ffuycuVKq7qiwWDA1dX1tq6MzUYzAGqdGhtHGxy8HP7TSC0rMQsbJxtqDqjJ1SNXidweSeUulen4U0e6LupK629aY8w2suo1yxbf0JM3a4FrKwexefsOpDEPU2ocrs6O1pCNKpWGNm26cPGCjivRC9izZzPlypXj2PGhYJ7F/O7fMefFb0g1ZNKl8vO46vzJlWpCw88TfukCkZciOTQrlNTkFNzc3HCxd8aYY8LDw4OePXty6NAhXnrpJaZPn87+/fsJDw/H3t6e3bt3ExJimYROnz6Nu7s7FSpUwMnJieDgYM6d+y+J9+ODMgEoKDwipJQs7bOQrPgsnFrFEDy9Ok2bNkWn0+Hv709GRgY7duygbt26TJs2jT179uDg4ECzZs0IDQ1Fp7P4CRo7dix//PEHGo0GKSXbtm0jPDwcJycntm7dilptCXoyfvx4qlWrxu7duzl48CAqlYqvvvqK5s2bo9VqrUZNBdqYb4x1py6cbZxsCjiBy07KRqVRUef1OnjX8MY72Ju6b9Ql/lQ8adFp+JxNRptpieVrr1bR0MUelaMTuue7YfvhECpM/ZLffvyhgLrniBE/k5hYjrffOsfkye/QLGQF8fHrWfpnIt3/fIveS97jva8+xcZZ8H7j1/FyK8HHz3dDCHDSaVFFJKLP1WBjY4PRJo+87DxSUlKs8X+3bNmCjY2NVcc/Li6OGjVqoFJZhsetW7fi5OREeno6RqORU6dOUaZMmXv9GRQrlJCQCgqPiJiDMfxc52e0FWOxqXGFrFLlMTk4M3r0aMxmSZlqddEENsJ4dhvG1DhKly7N8ePH0Wq12NrakpqaiqurK5mZmTg5OREfH4/JZKJx48bs3LkTk8lkvZetrS0VKlTg5MmT6HQ6srKyKFmyJNnZ2ZQpU4aIiAjeffddPvnkkwJtPP3XaZZ0XcJrh1+7pdy/QP6/T7OkyxIAtPZahp0ehnOpgoFsEs8lMiN4hjUwfEx1d7pt6E1oWgazriQA8E2FkrziVzCY+41IKUlO2UvkpZ9ISrZsTNeo/jMuoiFxPx6xWKZmGQFYXeo4U7+byNX4WLQaFXqtPS3KNGHw268zaOibZGRnYvaW5OTkEBgYSOXKlWnTpg3ff/89er2ekydPsmbNGurXr09GRgZBQUH89NNPfPnll+Tl5dGvXz+GDBly22dTnLhVSEjFGZyCwiMiL//Nt9wz5VF5uLNj335EuSoA/LD1PNcSk3ArpyE+4hwff/Au5cqVY/DgwRgMBvr168fUqVNJT09nwIABTJ06FSEEzz77LLVr12bbtm04OzuTlpaGlJLs7GzCw8MxmUwYDAbs7OyIj48nLy+P9PR0AgIC2LhxI7Nnz6ZUqVJkZGTw7rvv0qRsEwDSY9LvaAIo17Yc7hXcMZvMdPuj202DP4B7eXe6Le3GykEr0Tjq4FgiP7y1mr2vVaWKvZ5Py/nS0t2pkNr/QQiBm2tD3FxvjpXg+mIFEn89hUljRG3U0JSy1JryLbsXz8eYl8vgSbMxrI3DcDKZTyu+S/TZRN67/L+b6unZs+dNaQ4ODkRERABYXUc/SSgTgILCI8KppGWQS4lIpFLdQEocCmPpli1sPRXDhD+346qDpvrLrBRmNq/dTny5PMxmM0IIVq5ciY+PD3q9np9++sm62XvlyhVatWqFs7MzXl5eGAwGDAYDOp0Oo9GIjY0NBoOBwMBA4uPjyc7OJiUlBRcXFyZNmkSVKlXQ6XSkpaVRo0YNDm89DEB6bPod9Ulnr2PIiSGoNKr/jGNQvn15RkSPAOC3QcvhlyMMeLsxz9Utc9/xD/SV3FA769A5O5IadRVxMY/t22ejt3egRb9XcfLxxNzLnbgfDqORgjyD8b7u9ySh7AEoKDwinEo5odJB4ukUwlb+hZ1OS8tGjXjumdbYbX2LUR/nkpETg4ODAweP7eXPDQsRKhVarRatVovJZMLW1pa8vDyL8ZiUhIeHM3v2bBo1akRERAQ5OTmoVCref/99hBDY29sjpeTYsWNcvXoVBwcHVCoVOTk5NGnShFOnLBo5mZmZVK1aFQcfi0O3tMtpd9wvtVZ9V4N4cCtLgPiSJ5MeSPAboRI4NCtJblQ6ttjjqHXDVu3I4B9nU7215a1dZaPGvXdltDo1eblGzCbzfd/3SUCZABQUHhFqrRq1HqTR8m+ntdFj5+RDndff5IcfvahY0YaGdZMIDCzHcy1fIs/FAZPJhC6gPCV0OrIyM0lPT6d06dIIIXj33XepXLkyarWaxMREPvjgAwCWLVvGuXPnqFKlCrm5uTg5OdG9e3ccHByIjY2lWbNmHDx4kLJly/LSSy8REhJCUFAQnTp1QmOjwTPIk4jNEffUR2mWHF94nAM/HeDQL4fYO3kv277Yhtlkxmwys37Eev56+S8ATv5+excRd4pjYz+83qmFqo4DV7MjkFqJzrag9pLWyx69uy1SgjFfHPe0o4iAFBQeEbsn7CYvDXQB2QDkGXIQrh4MqLYAg1GHWv0M1WssY/bsa7zUvCeurbsyZ+JQ0s6Hs1dvi5/BgBozMdfi0ev1jB07ljlz5tC/f3+2bt3KihUr0Ol0PPvss/Ts2ZOBAwcSHh6OwWBgz549VtVKBwcH5s+fT25uLs7Ozixbtgyz2UzdunXp3r07AW0CCJsWhjTLuw7ofmnbJf7q9ddN6f4t/dk7cS+n/zpNnSF1MKQaqNS50v0/1BvQetnj+2JNMkpm0rN0oa7FcG1ZGladITc7D52TzQO9/+OIMgEoKDxEEs8lEnswlsu7L7P/h/1U7lqZLgs/Ydv8n9E7OHLYQYWfPhaTpgYNggeyb98a2rb1Ye78mSTpNTh/9g3Z2zaSvXQBJp0am4xoDAZJvXr1WLt2LXq9Hq1Wi5+fH6tWrUJKSaNGjZBScuDAAfz8/Lh06RIAXl5eqFQq9u7dS0REBGPGjGHUqFG4uLhgMBjQ6/Xo9Xq8qnthzDESfzoez6qed9Vf7xqWjeOag2rS7NNmJJ5NZH7b+SzsuBBDqoFnJj1Dg7caPOjHXIAKDZrc8pre3bIqyE3PBa+H2ozHAmUCUFB4SIRND2P10NUWZydAUK8gnp3xLBqdhlYDLGqEQdlZ7DmwCo1xHwfCXkClhnb169FMO4A87zPMst9K/Mv98crOxhj2JyqVipdeeonlO48zZMgQli5dikajYffu3Xz33Xe8+eabfPbZZ/Ts2RNvb29mzJhBTEwMOTk5uLu7o1arUalUuLq6MmHCBPR6PS1btsRgMDBy5EhsbGywdbP43M+7BzGJrZstZZqVIWZ/DC5lXHAp40KXhV3YP3U/NQfUpNagWg/s+d4LOkeLLUVuxp27qX6SUSYABYWHQHpsOhvf30jZkLK0m9IOGycbXMq43JTPydaO1o3nkJC4BaMxjTNHVmOmIZsqLCekVTCviqb0cPZEU3EDmmB7eHYS1OnP5/+qZ9OmTdbjzz//5+r1QOl3w3X/Pk6l/ls181Z4VPbg4IyDZFzNwMHbgaCeQQT1DLqnuh40OgfLBGBINxRxS4oHygSgoPAQ2PLxFky5Jp77+TncAv872LtabYOXZ3sA/Hx7APDivzO9txfMRvC6tVvlB0XalTRUWtU9BXjf9e0uDs44SPkO5YtlnGMbR4vcPzddWQGAMgEoKDxwovdHc2TuERr/r/FtB/87pkTFB1PPHWDMMaK11d61iubJJSfZ9L9NVHupGi/8+gIqTfFTMrwuAlJWABaK319IQeExJuNqBssHLMfB24GmnzQt6ubcE6ZcE2qd+q7KGHOMLH1pKQCVulRCrb278o8KZQVQEGUCUFB4QKReTuWHSj8QfzKe1t+0tg42jxvmPDMq7d0NDUIt0DvrAVjafak1GlhxQ1kBFESZABQUHhBrhq3BlGui17pe1Ohbo6ibc8/cywpArVXTb1s/+m3vh4u/C2E/FU+njcoKoCDKHoCCwgMgJyWHsyvP0uTjJgQ+E1jUzbkvTLmmexLheFW3KNa7+ruSnZz9oJv1QFBpVGj0GmUFkI+yAlBQeABs+nATCKj4/KPbrH1YCCGsrpvvhIyrGVwKvWQNJpN0PgmPSrd27VzU6Bx1ih1APncSFF4vhNgvhDgqhDgphBiTnx4shNgrhDgihAgTQtS7RflLQojj1/PdkO4mhNgohDiX/+364LqloPDoSItO49DPh6g/vD4l6xceAP1xwq+BH+kx6aREptw2b25mLhN8JjCvxTwitlj8B2UlZBVLFdDr6Bx0iggonztZARiAllLKGkAw0E4I0QD4FhgjpQwGPss/vxUtpJTB/wpI8CGwWUpZHticf66g8NhxceNFpFlSc8BNobMfS8o0tUS7itoZddu8GbEZ1mOhtngo1bvoyYrPemjtu19sHG2UFUA+t50ApIXrf2Vt/kfmf66bCjoDMXd5707AvPzjecALd1leQaFYELE5ArsSdnhWuzu/OcUVzyBPdI66O5oAbG5wqPbXy38xI3gG6THp5GYW3wFW56isAK5zR5vAQgg1cBAIBH6UUu4TQrwNrBdCfIdlIml0i+IS2CCEkMAMKeXM/HQvKWUsgJQyVghR6H+PEOJV4FWA0qVL31mvFBQeEVJKLm6+iH9L/7v2nFlcUalVlGlahnOrz2HK++8NYbsSdqg0KsxGM5lxmegcdHSc3rHYuH4oDJ2Djuyk4rlJ/ai5o01gKaUpX9RTEqgnhKgGDAHekVKWAt4BfrlF8cZSylpAe2CYEKLZ3TRQSjlTSllHSlmnRIkSd1NUQeGhk3AmgYzYDPxb+Rd1Ux4odYbUIe1yGid+P/Gf+YQQBLQJwK28G5+ZPuPN829S57U6BVYGxQ1FBPQPd6UFJKVMAUKBdsArwHXH338AhW4CSylj8r/jgL9vyHdNCOEDkP8dd3dNV1AoeozZT2Z4wfIdy+MZ5Mm20dtu+7bs6OdITnIOQiUeSISvh40iAvqHO9ECKiGEcMk/tgVaA2ewyPxD8rO1BM4VUtZeCOF4/RhoC1x/pViBZRIh/3v5PfdCQaGI8K7pDQIiQyOLuikPFCEEHad1JPVyKr+1/Q1TnumWed3KuZGVkEXyxeRH2MJ7R+egw5BuQEpZ1E0pcu5kD8AHmJe/D6AClkgpVwkhUoDJQggNkEO+nF4I4QvMklJ2wBJy4e/8twINsFBKuS6/3nHAEiHEQCAKuHu/tQoKxQCdgw6K/4vvXVO6SWm6LuzKH93+YMunW2jzTZtC89XoW4Otn21l2+fbeGHuC4+2kfeA3lWPIdXAF5ov0DnoLB9Hy7eNo02B83+nq3VqVFoVaq0alUb1z/GtvjUqy96QsEyq17/vNO36JKXRa6yuNh4kt50ApJTHgJv026SUO4HahaTHAB3yjy8ChdrESykTgVZ32V4FhWKFEILy7csTviL8vuqRUhZL8UmVF6tQvU91do/fTaN3G2HvaX9THkdfRxq+25Bd43ZRvXd1AloHFEFL75w6r9exrALSDOSm55KbkWv9NqQbSItOK3B+L4FxHjRCJXjt8GtWa+sHheIKQkHhPrFxtkGa5W01ZgrDaDCydvhajs47SqmGpei1rhcam+L1b9nw3YYcX3Ccv3r9xUvLX0Jrp70pT8hnIZz+8zRrhq1h2JlhxXIyu46jjyON3298x/mlWZKbaZkQTLkmzHlmTHmWb7Pxn+NCv41mkJY6pJSW4+vfd5AmhODasWscnHGQvOwHPxEVr1+agsJjSPkO5Tn08yEubrpI+fbl77icKc/E751+58L6CwS2C+T8uvOc+fsM1V6q9hBbe/d41/Dm2RnPsnLwSvb/sJ/GH9w8eGpttTT9uCnL+y8n5kAMfvX8iqClDwehEtg42hSZd9cjc49wcMbBQldf94viC0hB4T7JuGaxk3Qu5XzHZaSUrB6ymgvrL/Dcz8/x8uqXcSnrwsEZBx9WM++LWoNqUbpJabZ/uZ1rx64VmqfSC5WwcbJh7ZtrrX6BFO6f9Jh0ABy87j5C2+1QJgAFhfsk9lAsOgcdJareuZ3K4dmHOfzLYZp+2pRag2ohVIKg3kFc2nap2Oqod/29Kzp7HWuGrSn0ut5FT4dpHYjeH83Z1WcfceueXJIuJOHg7VCo6O1+USYABYX7xDvYm9yMXBJOJ/xnvpiDMSxov4BfW//KuuHr8KvnR4vPW1ivB7QKAAmn/z79sJt8Tzj5OVH/7fpE7YziwoYLheap3LkyAPEn4x9l055oks8nP7jQov9CmQAUFO6T6y6g/+utV0rJ9s+3c37defKy8ghoE0Dn3zoX2CwtE1IGR19Hzq85/9DbfK/UH14fj0oeLB+wnPPrbt1OoS6+m8CPE9IsuXbsGh5VHo57bWUCUFC4T5z8nLArYUfCmcJXAEaDkS2fbCF8RTgtvmjBwN0DeWnZS7hXcC+QTwhBmZAyXNp2qdgaKWlttXSe35n06HQWdlxIyqWUAteFSiDUgpSIlELLK9wdiWcTyUnJoWSDh+NmXJkAFBQeAGWaleHk4pNc3nO5QHpOag4L2i1g59idBL0cRNOP/ztQfOmmpcmIzSA1MvVhNve+8K3ti28dX2zdbW/y+6/RawhoHcCROUfISiy+LqEfF67svQKgTAAKCsWZ9lPb4+DtwJwmc9j2+TaklMSExfBLw1+I2hlF598602VBl9t6DL2+0VfcQxamRafh39LfYgX9L5qPbo7ZaGZhh4VkxmcWQeueHK7svYKNsw0eFRURkIJCscXRx5FBewdRrWc1QkeF8n2p7/m57s9kJWTRe0Nvqveufkf1eAd7A3Ap9NJDbO39IaVE76wn42pGoddLNihJ9z+7c+3YNf7s+WexFWc9DlzZe4WS9Us+NFfjygSgoPCAsPOwo/NvnWn5VUtKVC5Bm+/a8ObZN/Fvceeuor1reONU0onLOy/fPnMREbElgoQzCQT1urXPf7PRjM5BR8TmiFtOFAr/TW5GLnHH4/Cr//CM6hRLYAWFB4gQgqYfN72trP8/61ALTi45Sc2BNf/f3r2HR1Wdix//vsmE3CAJBALhFggiQoAECJACIgYsFEWqqChIvZ2iVlvR9lhqT+ul0qrHWn368/jTihaeKso5glwEAXMIURAkQYIRApFLuAQJ5Abkflnnjz2JgUzITEhmJuT9PM88mdmz1+x3VpL97r322msx4McDWjC6y2eMIfnpZDr16kTsPIfDfAGQ9FQStgAbcz6ZQ6fITm6M8MqRk5aDqTGt1v4PegaglNeJfyiewPBA3r/xfdY9us6rbgzL2ZnDsa3HmPC7CdgCGj9+DOkVQnD3YAZOd35oDPWDM5ln+PiejwFa9QxAE4BSXmbCwgk8su8R4u6PI/WNVJbdvMxr2tG3/207YI0Aeil9JvTh+93f890G772nwVtVV1az7OZlFGUXETkykqDwoKYLNZMmAKW8UHC3YGa8OYOpf5vKkc1HOP7lcU+HBPwwLs3yW5ez5sE1jU7+PvaXY4kYGsH7N77Plj9toaZaxwZy1s7/2kl+Vj6Jf05k7vq5rbotvQaglBfrM74PACVnvKNP/eyPZ1NTWcO2l7ex7eVtlBeVc9sHtzVYLzgimPtS7mPtQ2tJ/mMy2Vuymb1itlfPFewtMldk0n14d679XfOvIzlLzwCU8mJVZdacw8W53tGfPrBzIMERwUx5cQrdhnRj/+r9jTZP+Yf4c+t7t3Lz4pvJ3pJN0lNJbo627Tm27RjZKdnEzI5xy/Y0ASjlxUJ6hyC+wpqfr2HN/DVUlXvHJPRpb6Zx+tvTJC5KvOTkLyLCiPtHEHNHDBnLMqiuaHxuYQW7/rEL/xB/xj421i3bc2ZS+AAR+UpE0kXkWxF51r48TkS2i8huEUkVkTEOyvYRkc0iss9e9rF67z0jIifs5XeLyPSW/WpKtX1hUWE8lP4QCU8ksOsfu1j8o8XsXrLboxeFTY1h239uo/ePepOwIMGpMsPmDqM0v/SSA8gpqCypJKBzAB2CG95h3RqcOQMoBxKNMbFAHDBNRBKAl4BnjTFxwB/try9WBfzaGDMYSAAeEZEh9d7/mzEmzv5wPMi4Uu1cREwEU/86lTs+uoPK4kpW3buKlD+leCyevKw8Cg4VMOL+EU5P/Rh9QzRB3YLY9fauVo6u7So/V07W+iyiro1y2zabTADGUnsrn5/9YeyPEPvyUCDHQdmTxphd9ufngH3AlTNXnFJuNPjWwTyS+QixP4sl+elk9ry3xyNxmGrr7MOVCUp8/XwZ8+gYDqw5QE5ag11Fu2RqzAUzp6UvTafiXAWjHx3tthicugYgIr4ishvIBTYZY3YAC4D/FJFjwMvA75r4jH7ACGBHvcWPisgeEXlHRDq7Hr5S7YuIcNNbNxF1XRQr717Js/Isi3+0uMGwzK0prH8YPjYfcjNyXSqXsCCBwC6BfPbbz7zmvgZPyfw4k0VBi1h+23LASgY7/99Oeo7uSe+xrXfn78WcSgDGmGp7U09vYIyIDAUeBh43xvQBHgcWN1ZeRDoCHwELjDFn7YvfAAZgNSudBP7aSNn59msMqadP6yxDStn8bcxZO4e4e+MIDA8kNyOXlfNWYmoa7lSrK6opKypr0e37BfrRPbZ73VDFzvIP8WfSc5M4nHSYzX/YTGVJJVnrsti/en+LxufNjDF89fpXfHjLh1SXV1NZXAlAxgcZnMk8Q8Ljzl1TaSniaiYWkaeBYuAPQJgxxojVEFhkjAlxsL4fsBbYYIx5pZHP7AesNcYMvdS24+PjTWpqqkvxKnWl271kN6vuXcWwucOY/vp0AkID2POvPaz/1XqqyqqoKq0iMDyQyJGRJC5KpGd8T6fb7huz/LblZK7M5Pdlv8fXz9fpcjXVNax+YDXpS9LrlvmH+rOwcOFlxdNW7P7nblbdt6rudb/r+zF3/Vxev+Z1AsICmJ82v1VG/hSRNGNM/MXLm7wRTES6AZXGmEIRCQSmAC9itflfByQDiUCWg7KCdWaw7+Kdv4hEGmNO2l/eAmS49I2UUgDE/iyWs8fPsvk/NvPNe9/QPbY7JadLKCsoY+xjYwmOCKbgUAH7V+/n7TFvI75Cj7gejPv3cQy5bQg+vq73Bh88azD7PtpH5seZxNzufJ91H18fZr47k+HzhnNw40FObD/BiZ0nXN5+W3U46TAA96bcy8p5KwkIDWDn6zspPFLIvE3zWm3Y58Y4cydwJLBERHyxmoyWG2PWikgh8JqI2IAyYD6AiPQE3jbGTAfGA/OAb+zXEACesvf4eUlE4rAuJh8BHmypL6VUeyIiTPz9RAb+ZCB7/rWnbryeKS9OYfyT4+vWKz9bTtpbaRSfLubA6gN8dOdHJA9KZu66uXSOdu0SXMztMSQ/nUzKn1IYMmuISzsuESF6cjTRk6P5bOFnHN/hHcNctLbKkkq++/Q7ulzVhb7j+xLYJZCio0WkPJ/CgKkDiJ4S7faYmkwAxpg9WBdvL17+BTDKwfIcYHq9dRz+ZRhj5rkarFKqcZEjI4kcGVmXAIbeeWGLqn+IP+N+Mw6AKX+Zwr6V+1g7fy1LEpdw75Z7CYsKc3pbPjYfrvvjdayct5J9K/cxZNaQpgs54BfkR3V5NdUV1fh2cL4pqS3a9OQmSs6UMH7heMRHCO0byv5V+0GsZO0JeiewUleYX+z9BT9d8lNC+4Y2uo74CENmDWHepnmUF5WzdPJSSgtKXdrO0LuGEn51OFue2eLwArQzamPMP5jfrPJtSflZa5rP0Q9b3Tx9bNbuN/ZnsfSI7eGRmDQBKHWF6Ta4G7E/a3yylvoiR0Yy55M5FB0tYvMfNru0HR9fHyb+cSK5Gbns/Whvc0Klf6I1W1rmysxmlW9L8rPyQaCs0OqVNe4344i7L47Jf5nssZg0ASjVzvUZ14foKdFkp2S7XHbonUPpek1Xkp9OvuCmJmeF9g3Fx+bjVZPetIbyc+XkfpvLsDnD6uZS6J3Qm5nvzPTojGmaAJRS9IzvyelvT1N+rtylcj6+Pkz+y2TO7DvDzv/a2axtG2Pc3vvF3UrzS6k4V0FZQRnZn7ueaFuLJgClFNE3RGNqDAfWHHC57KCZg4i+IZrkZ5KbNVqpqTGNdBW5MlQUV5DyvDV2U9a6LP73qf/1cEQ/0ASglKLv+L6E9A7hm/e/cbmsiJCwIIGygjIOfXbI9Y0bLvvGNG+W+kYqX7/9NWBdfB8wbYCHI/qBzgimlEJ8hJg7Y/jy5S9Zcv0SOkZ2ZPKfJxPWL8yp8v0T+yM+wpHNR7j6xqtd27avXNFTRp7cdZKQ3iEsOLrASnZe1NylCUApBcDIfxvJ9le2cyT5CH5BfhQeLuT+rfc7tcPav2Y/psbQY4Tr3Rk7BHeoGxPnYruX7L5gPuRBNw/iqmlXUZhdiM3f1uTk9J5WWVrJwY0H6TnKPvyG9+z7AU0ASim7roO68lD6Q5TklVCUXcTH93xM+tJ0Yu+JpeBgAbYAGyG9Gwz3BcCO13YQPii8wc1nzgjqFsS5E+ccvvfFn7+g4HABgV0CKT5VTNqbacQ/HE/qG9aYYHdvvJsBN3hPk8rFinOLKc0r9apmn/o0ASil6kQMjQCsnjlbX9zK7nd3c3DDQTI+sIbq+vErP2bsL8fW3cRUq7qimuCI4GaNKxQ5MpITX52wegNddC0gYmgEZYVl/Drn12x9aStJv0si9Y1UOg/oTMHBAoqyi5r5TVtXTVUNxbnFhPYNJahrEKfST3k6JIf0IrBSqgER65pAdko2GR9kkPBEAr3G9mLjExt53v95vnjxiwvW7zm6Z7N3ctFToinKLiLrkyyqyqqorvxh3uAeI3pQnFtMTXUNExZOYOqrU4m+IZoZb80Afri71tusnLeSV3q9wnM+z1FypoRuQ7p5OiSH9AxAKeVQwoIEcr7KIWJ4BJMXTeb8qfPsfnc3W1/cyo5Xd5DwWAK2AGsX4mPzaXb7dty9cex4bQfrHlkHAiG9Qrjvi/sctpknPJZAwmMJdUNPeGMCKMkrqTtjqtXv+n6eCaYJegaglHLIv5M/d625i8mLrKEKOnbvyISFE7jpzZs4//15Vj+wum5dv0A/qkqrmjXTl28HX2b8YwZFR4soyi7i2LZjJD+TDEB1ebXDMuIjdOjUwesSQP53+bzc/eUGy5vVPdYNNAEopVwSc0cMCU8kkPFBBsWniwEI6hpEdUU15UXN2yH3GdeH+IfjQayj5ZTnUlh28zK+fOVLeo3t1eCaA1ijm3pbAlhx94q6OZPry1zhnWMdaQJQSrls+NzhF9w53PWargCc+qb5FzunvTqNB3c9yLyN85j4h4l8t/47fP18mfnOTIc3inljAoiaGAVQ1zQGMPDGgR4d8O1SNAEopVzWY0QPQqNCSV+STlVZFZGjIgE4mXayiZKN8+3gS4+4HvjYfLj+uev5bcFveeLEEw4voJoaw5l9Z7wuASQuSsQWYKOqzBoSI6BzAHPWzvHIZC/O0ASglHKZiDD+yfFkp2SzKHAR7017D1ugjZzUnBb5/POnzrPnX3vY9tdtFB1r2NWzdkJ6bxtF1NfPlwe2P1D3+palt3gwmqZpLyClVLOM/sVoOvboyMGNB0l7Mw2AwiOFl/25Bz6xpqus3bl//vznLDi6gI7dO9atExgeCEDcfXGXvb2Wkncgj6/f/ZpjW48BVoxX3+TasBjupmcASqlmG3zrYKa9Og1boHUsGTPb+QniHTE1hjX/tobO0Z15OONhbvvwNnxsPiz+0WJ2/H1HXS+j2pnEzn9//vK+QAtacfcKtr6wlaOfH6Xf9f2Y9f4sT4fUpCbPAEQkAEgB/O3r/48x5mn7hO7/HwgAqoBfGGO+clB+GvAa4Is1WfwL9uVdgA+BfliTwt9hjCm4/K+klHInW4CNn3/1c3LSchg2Z9hlfdapPac4//15prw0hYiYCCJiIujYoyNrH1zLp7/6lB5xPYi6Ngq/QD+CI4Jb5IyjucrPllNwuICCQ9YjZ6fV/DXrg1nE3BHTJkY4daYJqBxINMacFxE/4AsRWQ88BzxrjFkvItOBl4BJ9QuKiC/wOnADcBzYKSKrjTF7gYVAkjHmBRFZaH/925b6Ykop94kYGlE3jMTlKCuypksM6fXDmENRE6O46a2b+OfEf1J4uJCoa62eNr0TenNg9QGq/l51Qa+blmKM4VzOOfIO5NXt5AsPFdY9LzlTcsH6tgAbY341hqGzXR8PyVOarDVjnXPVnmf52R/G/qj9LYUCjq7+jAG+M8YcAhCRD4CZwF77z0n29ZYAyWgCUKpdqyyxRgU9uetk3XzBZ0+cZcmkJYDVq6bWqAdHsX/1fjJXZV72TrfoWBGHkw6Tl5VH/oF862dWfl08YN3tHBoVSufozgyeNZjO0Z3rHmH9wwjsHHhZMXiCU2nTfiSfBlwFvG6M2SEiC4ANIvIy1rWEcQ6K9gKO1Xt9HBhrf97dGHMSwBhzUkQcHj6IyHxgPkDfvn2dCVcp1UbVDj1dv+tnUNcgTI0hclQkg2YMqluel5UH0OgIpc6qLKlk6eSl5Gfl42PzIax/GOEDw+l3fT/CB4bTZWAXulzVhdA+oQ5vSGvLnEoAxphqIE5EwoCVIjIUa6f8uDHmIxG5A1gMTLmoqKNGMJfuFTfGvAW8BRAfH+/6feZKqTYjINR+hF9vz5HyJ2s6xeBuwXXLjDHseHUHUROj6Dv+8g4Ml928jPysfCY8NYFJz0zC18/3sj6vLXEpnRljCrGaaqYB9wAr7G/9N1Zzz8WOA33qve7ND01Fp0QkEsD+M9eVWJRSV54OHTsAF/bvP7jhID1H9+TO1XfWLTu99zSFRwoZetflNf1UnK/gcNJhAPpf379d7fzBuV5A3YBKY0yhiARiHeW/iLUjvw4rISQCWQ6K7wQGikh/4ARwJzDH/t5qrCTygv3nqsv6JkqpNs8v2A/gghnCwq8O55v3v+HTxz4lrF8YQV2DOLD2AOIjXHPLNS5vo7K0kuryagLCAjiSfASA2//7dq+9W7c1OdMEFAkssV8H8AGWG2PWikgh8JqI2IAy7O30ItITq7vndGNMlYg8CmzA6gb6jjHmW/vnvgAsF5EHgKPA7S35xZRSbU/tGcC6R9ax8TcbEZG63ja1s4DV6p/Y/4Kbwy6loriCzJWZpC9N53DSYYwxVtORvamp19heLfcl2hBnegHtAUY4WP4FMMrB8hxger3X64B1DtbLA7xzhCSllEcEdQ1iyotTKDpaVHfTV2leKTmpORQctG4TCo0KZX7afKd63VQUV7DhiQ1kvJ9BxfkKwvqFMe7Jcfh28GXv8r2cyTwDWMNZt0c6FIRSymvUjjHkSEleCe9e+y6dIjsRFB7U5GfVVNewYu4KDqw5QOw9scTeE0vUtVF1PY0mPT2JrPVZ+If4E9S16c+7EmkCUEq1CUHhQRSfKq4bcrkpm57cxP5V+/nJ33/CmEcb9lERH+HqG717rJ7WpglAKdUmlOaXUppfSpeBXag4X8G5nHMU5xZTVVZlPcqrqC6vpqq8itN7T7P9le2M+eUYhzt/ZdEEoJRqEwoOWdcAkhYmsek3m5pc/5qfXsPUv01t7bDaNE0ASqk2IfzqcEbOH4ktwEannp0I6RVCcPdg/AL98PX3xeZvq/tpC7AR3D24TQzI5kmaAJRSbYJ/iD8z3pzh6TCuKFfWwBZKKaWcpglAKaXaKU0ASinVTmkCUEqpdkoTgFJKtVOaAJRSqp3SBKCUUu2UJgCllGqnpHbI1bZARE4D2Z6O4zJ1Bc54OggvovXRkNbJhbQ+GnK1TqKMMd0uXtimEsCVQERSjTHxno7DW2h9NKR1ciGtj4Zaqk60CUgppdopTQBKKdVOaQJwv7c8HYCX0fpoSOvkQlofDbVIneg1AKWUaqf0DEAppdopTQBKKdVO6YQwbiAiHwKD7C/DgEJjTJyIdADeBOKBGuAxY0yyR4J0s0vUiR/wNjAS6+9zqTHmL56J0n0uUR9zgX+vt+pwYKQxZrd7I3S/xurE/t5wrP+dEKz/ndHGmDIPhOk2l/gb6QfsA/bb39tujHnImc/UBOAGxpjZtc9F5K9Akf3lz+3vDxORCGC9iIw2xtR4IEy3ukSd3A742+skCNgrIsuMMUc8EKbbNFYfxpj3gPfsy4cBq9rDzh8arxMRsQH/AuYZY9JFJByo9EyU7nOJ/xmAg7XJ0RWaANxIrAlK7wAS7YuGAEkAxphcESnEOhv4yiMBeoCDOjFAsP2fPBCoAM56KDy3c1Af9d0FLHNvRJ7noE5+DOwxxqQDGGPyPBWbJzTxN+ISvQbgXtcCp4wxWfbX6cBMEbGJSH9gFNDHY9F5xsV18j9AMXASOAq8bIzJ91RwHnBxfdQ3m3aYAGhYJ1cDRkQ2iMguEXnSg7F5gqO/kf4i8rWIbBGRa539ID0DaCEi8hnQw8FbvzfGrLI/v/gI7h1gMJCKNcbRNqCqNeN0p2bWyRigGugJdAY+F5HPjDGHWjVYN2hmfdSWHQuUGGMyWjFEt2tmndiACcBooARIEpE0Y0xSqwbrBs2sj5NAX2NMnoiMAj4WkRhjTJNnzpoAWogxZsql3rc3adyKdZRfW6YKeLzeOtsAR0d+bVJz6gSYA3xqjKkEckVkK1azWJtPAM2sj1p3cgUe/TezTo4DW4wxZ+zrrMPqNNDmE0Az9yPlQLn9eZqIHMQ6S0ptanvaBOQ+U4BMY8zx2gUiEiQiwfbnNwBVxpi9ngrQAxrUCVazT6JYgoEEINMj0bmfo/pARHywLo5/4JGoPMtRnWwAhtv/f2zAdUB7+b9xtB/pJiK+9ufRwECcPGDSMwD3cXQEFwFsEJEa4AQwz+1ReZajOnkdeBfIAAR41xizx92BeUhjR/kTgeNXQjNYMzSoE2NMgYi8AuzE6jSwzhjziSeC8wBHfyMTgedEpAqr+fQhZ6+b6VAQSinVTmkTkFJKtVOaAJRSqp3SBKCUUu2UJgCllGqnNAEopVQ7pQlAKaXaKU0ASinVTv0f+XcQB5r8PbcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in MD\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 0 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12173449182985506 = MD std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11930697895142317 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for MD\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  6.5813\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by -0.01055 0.31916 HD avg vs true 0.32971\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.03368, should have been 0.03649\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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trqrdwJ8Cv7xB4wRj7BfwUeDHk2xO8mLgR4CzKzxnB+Ps1RdYeKZJkpcD3wecW9Ep147jwM3Du/muA56pqouT3qnPoFZYVV1KcjvwIAvvJLqnqs4kuW24fjfwm8B3An8wPDO4VBvwtyuPuVcajLNfVXU2yV8Bp4GvAx+sqpFvHV7Pxvy39dvAHyV5nIWXsN5VVRvyf8OR5E+ANwBbk5wHfgt4EfzfXp1g4Z18c8CzLDz7nPxxh7cISpLUii/xSZJaMlCSpJYMlCSpJQMlSWrJQEmSWjJQkqSWDJQkqaX/BacyS9eVboe7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for CO\n"
     ]
    },
    {
     "data": {
      "image/png": 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JLkvyQJLb5lk+3vdRVTXxYnLC+n8DxwEHAzcDJ8xa53Tg00zub3ge8MWVrnugdv4Y8JTu/WmrsZ192zq13p8xuTjhFStd90C/08OZ3LV/dDf99JWue8C2/g7w9u79euBvgYNXuvYltPVFwMnAbfMsH+37qKUewbeHvKiqbwF7hryY9u0hL6rqBuDwJEeMXeh+WrSdVXV9VX21m7yByf0bq1Gf3ynAG4CPAQ+MWdwy6tPOXwI+XlV/DVBVa7mtBRyaJMD3MgmC3eOWuf+q6lomtc9ntO+jloJgvuEs9nWdJ7p9bcOrmfzVsRot2tYkG4CfAzaNWNdy6/M7/SfAU5Jck2RrknNGq2559WnrJcAPM7n59FbgjVX1+DjljWq076Mhh5h4olm2IS+e4Hq3IcmLmQTBCwataDh92vou4Ler6rHJH5CrUp92Hgg8G3gp8N3AXyS5oar+59DFLbM+bf1pYBvwEuAHgKuTXFdVDw9c29hG+z5qKQhaGfKiVxuS/AjwPuC0qnpopNqWW5+2bgQ+0oXAOuD0JLur6hOjVLg8+v7ffbCqvg58Pcm1wInAaguCPm09F7iwJgfStye5B/gh4EvjlDia0b6PWjo01MqQF4u2M8nRwMeBX1mFfzFOW7StVXVsVc1U1QzwUeC1qywEoN//3U8CL0xyYJLvYTLS750j17kc+rT1r5n0fEjyj4FnAHePWuU4Rvs+aqZHUKtryIsl69nONwNPA36/+0t5d63CUR17tnXV69POqrozyWeAW4DHgfdV1ZyXJT6R9fyd/i5weZJbmRw++e2qWnXDUye5AjgVWJdkB/AW4CAY//vIISYkqXEtHRqSJM3BIJCkxhkEktQ4g0CSGmcQSFLjDAI1L8lJSU6fZ9mpST41a97le0YxTfK+JCeMVOfmJIePsS+1pZn7CKQFnMTkDuTN+/rBqvq1Za9m/n3NGVbS/rJHoDUnyUySv0zygW4c9492d9vueS7B9d1Y9l9K8mTgrcBZ3Vj+Z+3jvq5JsjHJAV1P4bYktyb5zanl7+r2eVuSU7r5p3Tzvtz9fEY3/1VJPp7kM0n+V5J3TO3r3iTruvfndG27OcmHludfTq2yR6C16hnAq6vqC0kuA16b5CLgvwNnVdWNSQ5jcsfmm4GNVfX6ebb1wiTbpqaPBj41a52TgA1V9SyAWYdw/lFV/ViSFwGXAc8C/hJ4UXcn7U8Avwf8wtS2fhT4JnBXkour6tujUCZ5JvAm4Mer6sEkT+39ryLNwSDQWnVfVX2he/+HwPlMhi24v6puBNgzWmWPUUmvq6qX75lIcvkc69wNHJfkYuAq4I+nll3R7e/aJId1IXEo8IEkxzMZUfKgqfX/tKp2dfu6AziG7xyO+CXAR/cMq1BVC41pLy3KQ0Naq2aPnVJMxqUZZEyV7kE/JwLXAK9jMrLrQrX8LvC5rgfxM8AhU8u/OfX+Mfb+g22wdqhNBoHWqqOTPL97fzbweSaHY74/yXMAkhya5EDgESZ/oS9Zd+z+SVX1MeDfMXkE4R5ndeu8gMkIkruAJwN/0y1/1T7u7k+BX0zytG67HhrSfjEItFbdCfyLJLcATwXe2z368Czg4iQ3A1cz+Uv8c8AJSzlZPGUDcE13LuFy4N9OLftqkuuZPCXt1d28dwD/IckXmIyy2VtV3Q68Dfjzrh3vXGLNEuDoo1qDkswAn9pz4naFa7kG+DdVtWWla5HmY49Akhpnj0CSGmePQJIaZxBIUuMMAklqnEEgSY0zCCSpcf8f/nCC/zJgj2kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 0.01\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for MD\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 2.588 OH seats out of 15\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the state map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  772147.75\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 1.962 1.753\n",
      "fractional expected GOP seats =  0.977  out of  8 seats for MD\n",
      "simpler expected GOP seats =  0.8678  out of  8 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "pygments_lexer": "ipython3",
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